2018 |
Stefani, Jacopo De; "e, Yann-A; Caelen, Olivier; Hattab, Dalila; Bontempi, Gianluca Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting Journal Article International journal of data science and analytics (Print), 7 (4), pp. 311-329, 2018, (DOI: 10.1007/s41060-018-0150-x). @article{info:hdl:2013/283230, title = {Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting}, author = {Jacopo De Stefani and Yann-A{"e}l Le Borgne and Olivier Caelen and Dalila Hattab and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/283230}, year = {2018}, date = {2018-01-01}, journal = {International journal of data science and analytics (Print)}, volume = {7}, number = {4}, pages = {311-329}, note = {DOI: 10.1007/s41060-018-0150-x}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
de Bony, Eric James; Bizet, Martin; Grembergen, Olivier Van; Hassabi, Bouchra; Calonne, Emilie; Putmans, Pascale; Bontempi, Gianluca; cc, Fran Comprehensive identification of long noncoding RNAs in colorectal cancer Journal Article Oncotarget, 9 (45), pp. 27605-27629, 2018, (DOI: 10.18632/oncotarget.25218). @article{info:hdl:2013/278063b, title = {Comprehensive identification of long noncoding RNAs in colorectal cancer}, author = {Eric James de Bony and Martin Bizet and Olivier Van Grembergen and Bouchra Hassabi and Emilie Calonne and Pascale Putmans and Gianluca Bontempi and Fran{cc}ois Fuks}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/278063}, year = {2018}, date = {2018-01-01}, journal = {Oncotarget}, volume = {9}, number = {45}, pages = {27605-27629}, note = {DOI: 10.18632/oncotarget.25218}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Carcillo, Fabrizio; "e, Yann-A; Caelen, Olivier; Bontempi, Gianluca International journal of data science and analytics (Print), 5 (4), pp. 301-302, 2018, (DOI: 10.1007/s41060-018-0123-0). @article{info:hdl:2013/311389, author = {Fabrizio Carcillo and Yann-A{"e}l Le Borgne and Olivier Caelen and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/311389}, year = {2018}, date = {2018-01-01}, journal = {International journal of data science and analytics (Print)}, volume = {5}, number = {4}, pages = {301-302}, note = {DOI: 10.1007/s41060-018-0123-0}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Carcillo, Fabrizio; "e, Yann-A; Caelen, Olivier; Bontempi, Gianluca Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization Journal Article International journal of data science and analytics (Print), 5 (4), pp. 285-300, 2018, (DOI: 10.1007/s41060-018-0116-z). @article{info:hdl:2013/311546, title = {Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization}, author = {Fabrizio Carcillo and Yann-A{"e}l Le Borgne and Olivier Caelen and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/311546}, year = {2018}, date = {2018-01-01}, journal = {International journal of data science and analytics (Print)}, volume = {5}, number = {4}, pages = {285-300}, note = {DOI: 10.1007/s41060-018-0116-z}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ioannidis, J P A; Bhattacharya, S; Evers, J L H; Veen, Der F V; Somigliana, E; Barratt, C L R; Bontempi, Gianluca; Baird, D T; Crosignani, P; Devroey, P; Diedrich, Klaus; Farquharson, R G; Fraser, L R; Geraedts, Joep Pm M; Gianaroli, Luca; Vecchia, La C; Magli, C; Negri, E; Sunde, A; Tapanainen, J S; Tarlatzis, Basil; Steirteghem, A V; Veiga, A Protect us from poor-quality medical research Journal Article Human reproduction, 33 (5), pp. 770-776, 2018, (DOI: 10.1093/humrep/dey056). @article{info:hdl:2013/272828b, title = {Protect us from poor-quality medical research}, author = {J P A Ioannidis and S Bhattacharya and J L H Evers and F V Der Veen and E Somigliana and C L R Barratt and Gianluca Bontempi and D T Baird and P Crosignani and P Devroey and Klaus Diedrich and R G Farquharson and L R Fraser and Joep Pm M Geraedts and Luca Gianaroli and C La Vecchia and C Magli and E Negri and A Sunde and J S Tapanainen and Basil Tarlatzis and A V Steirteghem and A Veiga}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/272828}, year = {2018}, date = {2018-01-01}, journal = {Human reproduction}, volume = {33}, number = {5}, pages = {770-776}, note = {DOI: 10.1093/humrep/dey056}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Kieken, Fabien; Loth, Karine; van Nuland, Nico N A J; Tompa, Peter; Lenaerts, Tom Chemical shift assignments of the partially deuterated Fyn SH2–SH3 domain Journal Article Biomolecular N M R Assignments, 12 (1), pp. 117-122, 2018, (DOI: 10.1007/s12104-017-9792-1). @article{info:hdl:2013/272541b, title = {Chemical shift assignments of the partially deuterated Fyn SH2–SH3 domain}, author = {Fabien Kieken and Karine Loth and Nico N A J van Nuland and Peter Tompa and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/272541}, year = {2018}, date = {2018-01-01}, journal = {Biomolecular N M R Assignments}, volume = {12}, number = {1}, pages = {117-122}, note = {DOI: 10.1007/s12104-017-9792-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Skiba, Grażyna; Starzec, Mateusz; Byrski, Aleksander; Rycerz, Katarzyna; Kisiel-Dorohinicki, Marek; Turek, Wojciech; Krzywicki, Daniel; Lenaerts, Tom; Burguillo, Juan Carlos Flexible asynchronous simulation of iterated prisoner's dilemma based on actor model Journal Article Simulation modelling practice and theory, 83 , pp. 75-92, 2018, (DOI: 10.1016/j.simpat.2017.12.010). @article{info:hdl:2013/272556b, title = {Flexible asynchronous simulation of iterated prisoner's dilemma based on actor model}, author = {Grażyna Skiba and Mateusz Starzec and Aleksander Byrski and Katarzyna Rycerz and Marek Kisiel-Dorohinicki and Wojciech Turek and Daniel Krzywicki and Tom Lenaerts and Juan Carlos Burguillo}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/272556}, year = {2018}, date = {2018-01-01}, journal = {Simulation modelling practice and theory}, volume = {83}, pages = {75-92}, note = {DOI: 10.1016/j.simpat.2017.12.010}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ding, Li; Colaprico, Antonio; Olsen, Catharina; others, Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics Journal Article Cell, 173 (2), 2018, (DOI: 10.1016/j.cell.2018.03.033). @article{info:hdl:2013/279963, title = {Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics}, author = {Li Ding and Antonio Colaprico and Catharina Olsen and others}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/279963}, year = {2018}, date = {2018-01-01}, journal = {Cell}, volume = {173}, number = {2}, note = {DOI: 10.1016/j.cell.2018.03.033}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Thorsson, Vesteinn; Colaprico, Antonio; others, The Immune Landscape of Cancer Journal Article Immunity, 48 (4), pp. 812-830, 2018, (DOI: 10.1016/j.immuni.2018.03.023). @article{info:hdl:2013/279950, title = {The Immune Landscape of Cancer}, author = {Vesteinn Thorsson and Antonio Colaprico and others}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/279950}, year = {2018}, date = {2018-01-01}, journal = {Immunity}, volume = {48}, number = {4}, pages = {812-830}, note = {DOI: 10.1016/j.immuni.2018.03.023}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Bailey, M H; Colaprico, Antonio; others, Comprehensive Characterization of Cancer Driver Genes and Mutations Journal Article Cell, 173 (2), pp. 371-385, 2018, (DOI: 10.1016/j.cell.2018.02.060). @article{info:hdl:2013/279953, title = {Comprehensive Characterization of Cancer Driver Genes and Mutations}, author = {M H Bailey and Antonio Colaprico and others}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/279953}, year = {2018}, date = {2018-01-01}, journal = {Cell}, volume = {173}, number = {2}, pages = {371-385}, note = {DOI: 10.1016/j.cell.2018.02.060}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Malta, Tathiane TM; Colaprico, Antonio; others, Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation Journal Article Cell, 173 (2), pp. 338-354, 2018, (DOI: 10.1016/j.cell.2018.03.034). @article{info:hdl:2013/279981, title = {Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation}, author = {Tathiane TM Malta and Antonio Colaprico and others}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/279981}, year = {2018}, date = {2018-01-01}, journal = {Cell}, volume = {173}, number = {2}, pages = {338-354}, note = {DOI: 10.1016/j.cell.2018.03.034}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Bontempi, Gianluca; Mauri, Giancarlo; Castiglioni, Isabella In-silico integration approach to identify a key miRNA regulating a gene network in aggressive prostate cancer Journal Article International journal of molecular sciences, 19 (3), 2018, (DOI: 10.3390/ijms19030910). @article{info:hdl:2013/270452b, title = {In-silico integration approach to identify a key miRNA regulating a gene network in aggressive prostate cancer}, author = {Claudia Cava and Gloria Bertoli and Antonio Colaprico and Gianluca Bontempi and Giancarlo Mauri and Isabella Castiglioni}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/270452}, year = {2018}, date = {2018-01-01}, journal = {International journal of molecular sciences}, volume = {19}, number = {3}, note = {DOI: 10.3390/ijms19030910}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Trepo, Eric; Goossens, Nicolas; Fujiwara, Naoto; Song, Won-Min; Colaprico, Antonio; Marot, Astrid; Spahr, Laurent; Demetter, Pieter; Sempoux, Christine; Im, Gene Y; Saldarriaga, Joan; Gustot, Thierry; `e, Jacques Devi; Thung, Swan SN; Minsart, Charlotte; Serste, Thomas; Bontempi, Gianluca; Abdelrahman, Karim; Henrion, Jean; Degré, Delphine; Lucidi, Valerio; Rubbia-Brandt, Laura; Nair, Venugopalan D; Moreno, Christophe; Deltenre, Pierre; Hoshida, Yujin; Franchimont, Denis Gastroenterology, 154 (4), pp. 965-975, 2018, (DOI: 10.1053/j.gastro.2017.10.048). @article{info:hdl:2013/269084b, title = {Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis}, author = {Eric Trepo and Nicolas Goossens and Naoto Fujiwara and Won-Min Song and Antonio Colaprico and Astrid Marot and Laurent Spahr and Pieter Demetter and Christine Sempoux and Gene Y Im and Joan Saldarriaga and Thierry Gustot and Jacques Devi{`e}re and Swan SN Thung and Charlotte Minsart and Thomas Serste and Gianluca Bontempi and Karim Abdelrahman and Jean Henrion and Delphine Degré and Valerio Lucidi and Laura Rubbia-Brandt and Venugopalan D Nair and Christophe Moreno and Pierre Deltenre and Yujin Hoshida and Denis Franchimont}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/269084}, year = {2018}, date = {2018-01-01}, journal = {Gastroenterology}, volume = {154}, number = {4}, pages = {965-975}, note = {DOI: 10.1053/j.gastro.2017.10.048}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Bogeas, Alexandra; Morvan-Dubois, Ghislaine; El-Habr, Elias E A; cc, Fran; Defrance, Matthieu; Narayanan, Ashwin; Kuranda, Klaudia; Burel-Vandenbos, Fanny; Sayd, Salwa; Delaunay, Virgile; Dubois, Luiz Gustavo Feijó L G; Parrinello, Hugues; Rialle, Stéphanie; Fabrega, Sylvie; Idbaih, Ahmed; Haiech, Jacques; Bieche, Ivan; Virolle, Thierry; Goodhardt, Michele; Chneiweiss, Hervé; Junier, Marie Pierre Changes in chromatin state reveal ARNT2 at a node of a tumorigenic transcription factor signature driving glioblastoma cell aggressiveness Journal Article Acta Neuropathologica, 135 (2), pp. 267-283, 2018, (DOI: 10.1007/s00401-017-1783-x). @article{info:hdl:2013/272529, title = {Changes in chromatin state reveal ARNT2 at a node of a tumorigenic transcription factor signature driving glioblastoma cell aggressiveness}, author = {Alexandra Bogeas and Ghislaine Morvan-Dubois and Elias E A El-Habr and Fran{cc}ois Xavier Lejeune and Matthieu Defrance and Ashwin Narayanan and Klaudia Kuranda and Fanny Burel-Vandenbos and Salwa Sayd and Virgile Delaunay and Luiz Gustavo Feijó L G Dubois and Hugues Parrinello and Stéphanie Rialle and Sylvie Fabrega and Ahmed Idbaih and Jacques Haiech and Ivan Bieche and Thierry Virolle and Michele Goodhardt and Hervé Chneiweiss and Marie Pierre Junier}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/272529}, year = {2018}, date = {2018-01-01}, journal = {Acta Neuropathologica}, volume = {135}, number = {2}, pages = {267-283}, note = {DOI: 10.1007/s00401-017-1783-x}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Serroukh, Yasmina; Gu-Trantien, Chunyan; Kashani, Baharak Hooshiar; Defrance, Matthieu; Manh, Thien-Phong Vu; Azouz, Abdulkader; Detavernier, Aurélie; Hoyois, Alice; Das, Jishnu; Bizet, Martin; Pollet, Emeline; Tabbuso, Tressy; Calonne, Emilie; van Gisbergen, Klaas; Dalod, Marc; cc, Fran; Goriely, Stanislas; Marchant, Arnaud The transcription factors Runx3 and ThPOK cross-regulate acquisition of cytotoxic function by human Th1 lymphocytes. Journal Article eLife, 7 , 2018, (DOI: 10.7554/eLife.30496). @article{info:hdl:2013/268207, title = {The transcription factors Runx3 and ThPOK cross-regulate acquisition of cytotoxic function by human Th1 lymphocytes.}, author = {Yasmina Serroukh and Chunyan Gu-Trantien and Baharak Hooshiar Kashani and Matthieu Defrance and Thien-Phong Vu Manh and Abdulkader Azouz and Aurélie Detavernier and Alice Hoyois and Jishnu Das and Martin Bizet and Emeline Pollet and Tressy Tabbuso and Emilie Calonne and Klaas van Gisbergen and Marc Dalod and Fran{cc}ois Fuks and Stanislas Goriely and Arnaud Marchant}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/268207}, year = {2018}, date = {2018-01-01}, journal = {eLife}, volume = {7}, note = {DOI: 10.7554/eLife.30496}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
`e, Nathaniel Mon P; Lenaerts, Tom; Pacheco, Jorge M J M; Dingli, David Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria Journal Article PLoS computational biology, 14 (6), 2018, (DOI: 10.1371/journal.pcbi.1006133). @article{info:hdl:2013/267360b, title = {Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria}, author = {Nathaniel Mon P{`e}re and Tom Lenaerts and Jorge M J M Pacheco and David Dingli}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/267360}, year = {2018}, date = {2018-01-01}, journal = {PLoS computational biology}, volume = {14}, number = {6}, note = {DOI: 10.1371/journal.pcbi.1006133}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Byrski, Aleksander; Świderska, Ewelina; Łasisz, Jakub; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin Emergence of population structure in socio-cognitively inspired ant colony optimization Journal Article Computer Science, 19 (1), pp. 81-98, 2018, (DOI: 10.7494/csci.2018.19.1.2594). @article{info:hdl:2013/270586b, title = {Emergence of population structure in socio-cognitively inspired ant colony optimization}, author = {Aleksander Byrski and Ewelina Świderska and Jakub Łasisz and Marek Kisiel-Dorohinicki and Tom Lenaerts and Dana Samson and Bipin Indurkhya}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/270586}, year = {2018}, date = {2018-01-01}, journal = {Computer Science}, volume = {19}, number = {1}, pages = {81-98}, note = {DOI: 10.7494/csci.2018.19.1.2594}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis Journal Article BMC genomics, 19 (1), 2018, (DOI: 10.1186/s12864-017-4423-x). @article{info:hdl:2013/268430b, title = {Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis}, author = {Claudia Cava and Gloria Bertoli and Antonio Colaprico and Catharina Olsen and Gianluca Bontempi and Isabella Castiglioni}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/268430}, year = {2018}, date = {2018-01-01}, journal = {BMC genomics}, volume = {19}, number = {1}, note = {DOI: 10.1186/s12864-017-4423-x}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Silva, Tiago Chedraoui; Colaprico, Antonio; Olsen, Catharina; Malta, Tathiane Maistro Aistro T M; Bontempi, Gianluca; Ceccarelli, Michele; Berman, Benjamin B P; Noushmehr, Houtan F1000Research, 7 , 2018, (DOI: 10.12688/F1000RESEARCH.14197.1). @article{info:hdl:2013/311579, title = {TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data [version 1; peer review: 1 approved, 1 approved with reservations]}, author = {Tiago Chedraoui Silva and Antonio Colaprico and Catharina Olsen and Tathiane Maistro Aistro T M Malta and Gianluca Bontempi and Michele Ceccarelli and Benjamin B P Berman and Houtan Noushmehr}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/311579}, year = {2018}, date = {2018-01-01}, journal = {F1000Research}, volume = {7}, note = {DOI: 10.12688/F1000RESEARCH.14197.1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Reggiani, Claudio; "e, Yann-A; Bontempi, Gianluca Feature selection in high-dimensional dataset using MapReduce Journal Article Communications in computer and information science, 823 , pp. 101-115, 2018, (DOI: 10.1007/978-3-319-76892-2_8). @article{info:hdl:2013/269455b, title = {Feature selection in high-dimensional dataset using MapReduce}, author = {Claudio Reggiani and Yann-A{"e}l Le Borgne and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/269455}, year = {2018}, date = {2018-01-01}, journal = {Communications in computer and information science}, volume = {823}, pages = {101-115}, note = {DOI: 10.1007/978-3-319-76892-2_8}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Willot, Quentin; Mardulyn, Patrick; Defrance, Matthieu; Gueydan, Cyril; Aron, Serge Molecular chaperoning helps safeguarding mitochondrial integrity and motor functions in the Sahara silver ant Cataglyphis bombycina Journal Article Scientific reports, 8 , 2018, (DOI: 10.1038/s41598-018-27628-2). @article{info:hdl:2013/272668, title = {Molecular chaperoning helps safeguarding mitochondrial integrity and motor functions in the Sahara silver ant Cataglyphis bombycina}, author = {Quentin Willot and Patrick Mardulyn and Matthieu Defrance and Cyril Gueydan and Serge Aron}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/272668}, year = {2018}, date = {2018-01-01}, journal = {Scientific reports}, volume = {8}, note = {DOI: 10.1038/s41598-018-27628-2}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Raimondi, Daniele; Orlando, Gabriele; Moreau, Yves; Vranken, Wim Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping Journal Article Bioinformatics, 34 (18), pp. 3118-3125, 2018, (DOI: 10.1093/bioinformatics/bty309). @article{info:hdl:2013/286895, title = {Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping}, author = {Daniele Raimondi and Gabriele Orlando and Yves Moreau and Wim Vranken}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/286895}, year = {2018}, date = {2018-01-01}, journal = {Bioinformatics}, volume = {34}, number = {18}, pages = {3118-3125}, note = {DOI: 10.1093/bioinformatics/bty309}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Saykali, Bechara; Nahaboo, Wallis; Mathiah, Navrita; Racu, Marie-Lucie; Defrance, Matthieu; Migeotte, Isabelle Distinct mesoderm migration phenotypes in extra-embryonic and embryonic regions of the early mouse embryo Journal Article BioRxiv, 2018, (Language of publication: en). @article{info:hdl:2013/282216, title = {Distinct mesoderm migration phenotypes in extra-embryonic and embryonic regions of the early mouse embryo}, author = {Bechara Saykali and Wallis Nahaboo and Navrita Mathiah and Marie-Lucie Racu and Matthieu Defrance and Isabelle Migeotte}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/282216}, year = {2018}, date = {2018-01-01}, journal = {BioRxiv}, note = {Language of publication: en}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Abels, Axel; Roijers, Diederik D M; Lenaerts, Tom; Nowe, Ann; Steckelmacher, Denis Dynamic Weights in Multi-Objective Deep Reinforcement Learning Inproceedings Proceedings of the 30th Benelux Conference on Artificial Intelligence, pp. 1-2, Springer, 2018, (Language of publication: en). @inproceedings{info:hdl:2013/291980, title = {Dynamic Weights in Multi-Objective Deep Reinforcement Learning}, author = {Axel Abels and Diederik D M Roijers and Tom Lenaerts and Ann Nowe and Denis Steckelmacher}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/291980}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 30th Benelux Conference on Artificial Intelligence}, pages = {1-2}, publisher = {Springer}, series = {CCIS series}, note = {Language of publication: en}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Colli, Maikel Luis; Nakayasu, Ernesto Satoshi; Ramos-Rodríguez, Mireia; Turatsinze, Jean Valéry; Brachene, Alexandra Coomans De; Lopes, Miguel; Santos, Reinaldo Sousa Do; Mateu, Miguel Jonas Juan; Raurell-Vila, Helena; "e, Rapha; Marchetti, Piero; Pasquali, Lorenzo; Metz, Thomas O; Eizirik, Decio L An integrated multi-omics approach identifies the type I interferon- induced signature of human beta cells Inproceedings 54th EASD Annual Meeting of the European Association for the Study of Diabetes, 2018, (Conference: (Berlin)). @inproceedings{info:hdl:2013/299741, title = {An integrated multi-omics approach identifies the type I interferon- induced signature of human beta cells}, author = {Maikel Luis Colli and Ernesto Satoshi Nakayasu and Mireia Ramos-Rodríguez and Jean Valéry Turatsinze and Alexandra Coomans De Brachene and Miguel Lopes and Reinaldo Sousa Do Santos and Miguel Jonas Juan Mateu and Helena Raurell-Vila and Rapha{"e}l Scharfmann and Piero Marchetti and Lorenzo Pasquali and Thomas O Metz and Decio L Eizirik}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/299741}, year = {2018}, date = {2018-01-01}, booktitle = {54th EASD Annual Meeting of the European Association for the Study of Diabetes}, note = {Conference: (Berlin)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Kieken, Fabien; Loth, Karine; van Nuland, Nico A J; Tompa, Peter; Lenaerts, Tom Chemical shift assignments of the partially deuterated Fyn SH2-SH3 domain. Journal Article Biomolecular NMR assignments, 2017, (DOI: 10.1007/s12104-017-9792-1). @article{info:hdl:2013/262899, title = {Chemical shift assignments of the partially deuterated Fyn SH2-SH3 domain.}, author = {Fabien Kieken and Karine Loth and Nico A J van Nuland and Peter Tompa and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/262899}, year = {2017}, date = {2017-01-01}, journal = {Biomolecular NMR assignments}, abstract = {Src Homology 2 and 3 (SH2 and SH3) are two key protein interaction modules involved in regulating the activity of many proteins such as tyrosine kinases and phosphatases by respective recognition of phosphotyrosine and proline-rich regions. In the Src family kinases, the inactive state of the protein is the direct result of the interaction of the SH2 and the SH3 domain with intra-molecular regions, leading to a closed structure incompetent with substrate modification. Here, we report the 1H, 15N and 13C backbone- and side-chain chemical shift assignments of the partially deuterated Fyn SH3-SH2 domain and structural differences between tandem and single domains. The BMRB accession number is 27165.}, note = {DOI: 10.1007/s12104-017-9792-1}, keywords = {}, pubstate = {published}, tppubtype = {article} } Src Homology 2 and 3 (SH2 and SH3) are two key protein interaction modules involved in regulating the activity of many proteins such as tyrosine kinases and phosphatases by respective recognition of phosphotyrosine and proline-rich regions. In the Src family kinases, the inactive state of the protein is the direct result of the interaction of the SH2 and the SH3 domain with intra-molecular regions, leading to a closed structure incompetent with substrate modification. Here, we report the 1H, 15N and 13C backbone- and side-chain chemical shift assignments of the partially deuterated Fyn SH3-SH2 domain and structural differences between tandem and single domains. The BMRB accession number is 27165. |
Gazzo, Andrea; Raimondi, Daniele; Daneels, Dorien; Moreau, Yves; Smits, Guillaume; Dooren, Sonia Van; Lenaerts, Tom Understanding mutational effects in digenic diseases Journal Article Nucleic acids research, 45 (15), 2017, (DOI: 10.1093/nar/gkx557). @article{info:hdl:2013/261811, title = {Understanding mutational effects in digenic diseases}, author = {Andrea Gazzo and Daniele Raimondi and Dorien Daneels and Yves Moreau and Guillaume Smits and Sonia Van Dooren and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/261811}, year = {2017}, date = {2017-01-01}, journal = {Nucleic acids research}, volume = {45}, number = {15}, abstract = {To further our understanding of the complexity and genetic heterogeneity of rare diseases, it has become essential to shed light on how combinations of variants in different genes are responsible for a disease phenotype. With the appearance of a resource on digenic diseases, it has become possible to evaluate how digenic combinations differ in terms of the phenotypes they produce. All instances in this resource were assigned to two classes of digenic effects, annotated as true digenic and composite classes. Whereas in the true digenic class variants in both genes are required for developing the disease, in the composite class, a variant in one gene is sufficient to produce the phenotype, but an additional variant in a second gene impacts the disease phenotype or alters the age of onset. We show that a combination of variant, gene and higher-level features can differentiate between these two classes with high accuracy. Moreover, we show via the analysis of three digenic disorders that a digenic effect decision profile, extracted from the predictive model, motivates why an instance was assigned to either of the two classes. Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm.}, note = {DOI: 10.1093/nar/gkx557}, keywords = {}, pubstate = {published}, tppubtype = {article} } To further our understanding of the complexity and genetic heterogeneity of rare diseases, it has become essential to shed light on how combinations of variants in different genes are responsible for a disease phenotype. With the appearance of a resource on digenic diseases, it has become possible to evaluate how digenic combinations differ in terms of the phenotypes they produce. All instances in this resource were assigned to two classes of digenic effects, annotated as true digenic and composite classes. Whereas in the true digenic class variants in both genes are required for developing the disease, in the composite class, a variant in one gene is sufficient to produce the phenotype, but an additional variant in a second gene impacts the disease phenotype or alters the age of onset. We show that a combination of variant, gene and higher-level features can differentiate between these two classes with high accuracy. Moreover, we show via the analysis of three digenic disorders that a digenic effect decision profile, extracted from the predictive model, motivates why an instance was assigned to either of the two classes. Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm. |
Raimondi, Daniele; cc, Ibrahim Tanyal; Ferte, Julien; Gazzo, Andrea; Orlando, Gabriele; Lenaerts, Tom; Rooman, Marianne; Vranken, Wim F DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Journal Article Nucleic acids research, 45 (W1), pp. W201-W206, 2017, (DOI: 10.1093/nar/gkx390). @article{info:hdl:2013/254250, title = {DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins.}, author = {Daniele Raimondi and Ibrahim Tanyal{cc}in and Julien Ferte and Andrea Gazzo and Gabriele Orlando and Tom Lenaerts and Marianne Rooman and Wim F Vranken}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/254250}, year = {2017}, date = {2017-01-01}, journal = {Nucleic acids research}, volume = {45}, number = {W1}, pages = {W201-W206}, abstract = {High-throughput sequencing methods are generating enormous amounts of genomic data, giving unprecedented insights into human genetic variation and its relation to disease. An individual human genome contains millions of Single Nucleotide Variants: to discriminate the deleterious from the benign ones, a variety of methods have been developed that predict whether a protein-coding variant likely affects the carrier individual's health. We present such a method, DEOGEN2, which incorporates heterogeneous information about the molecular effects of the variants, the domains involved, the relevance of the gene and the interactions in which it participates. This extensive contextual information is non-linearly mapped into one single deleteriousness score for each variant. Since for the non-expert user it is sometimes still difficult to assess what this score means, how it relates to the encoded protein, and where it originates from, we developed an interactive online framework (http://deogen2.mutaframe.com/) to better present the DEOGEN2 deleteriousness predictions of all possible variants in all human proteins. The prediction is visualized so both expert and non-expert users can gain insights into the meaning, protein context and origins of each prediction.}, note = {DOI: 10.1093/nar/gkx390}, keywords = {}, pubstate = {published}, tppubtype = {article} } High-throughput sequencing methods are generating enormous amounts of genomic data, giving unprecedented insights into human genetic variation and its relation to disease. An individual human genome contains millions of Single Nucleotide Variants: to discriminate the deleterious from the benign ones, a variety of methods have been developed that predict whether a protein-coding variant likely affects the carrier individual's health. We present such a method, DEOGEN2, which incorporates heterogeneous information about the molecular effects of the variants, the domains involved, the relevance of the gene and the interactions in which it participates. This extensive contextual information is non-linearly mapped into one single deleteriousness score for each variant. Since for the non-expert user it is sometimes still difficult to assess what this score means, how it relates to the encoded protein, and where it originates from, we developed an interactive online framework (http://deogen2.mutaframe.com/) to better present the DEOGEN2 deleteriousness predictions of all possible variants in all human proteins. The prediction is visualized so both expert and non-expert users can gain insights into the meaning, protein context and origins of each prediction. |
Byrski, Aleksander; Świderska, Ewelina; Łasisz, Jakub; Kisiel-Dorohinicki, Marek; Lenaerts, Tom; Samson, Dana; Indurkhya, Bipin; Nowe, Ann Socio-cognitively inspired ant colony optimization Journal Article Journal of Computational Science, 21 , pp. 397-406, 2017, (DOI: 10.1016/j.jocs.2016.10.010). @article{info:hdl:2013/256822, title = {Socio-cognitively inspired ant colony optimization}, author = {Aleksander Byrski and Ewelina Świderska and Jakub Łasisz and Marek Kisiel-Dorohinicki and Tom Lenaerts and Dana Samson and Bipin Indurkhya and Ann Nowe}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/256822/1/Elsevier_240449.pdf}, year = {2017}, date = {2017-01-01}, journal = {Journal of Computational Science}, volume = {21}, pages = {397-406}, abstract = {Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive features of a population, incorporating perspective-taking ability to generate differently acting ant colonies. Although our main goal was simulation, we took advantage of the fact that the quality of the constructed system was evaluated based on selected traveling salesman problem instances, and the resulting computing system became a metaheuristic, which turned out to be a promising method for solving discrete problems. In this paper, we extend the initial sets of populations driven by different perspective-taking inspirations, seeking both optimal configuration for solving a number of TSP benchmarks, at the same time constituting a tool for analyzing socio-cognitive features of the individuals involved. The proposed algorithms are compared against classic ACO, and are found to prevail in most of the benchmark functions tested.}, note = {DOI: 10.1016/j.jocs.2016.10.010}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive features of a population, incorporating perspective-taking ability to generate differently acting ant colonies. Although our main goal was simulation, we took advantage of the fact that the quality of the constructed system was evaluated based on selected traveling salesman problem instances, and the resulting computing system became a metaheuristic, which turned out to be a promising method for solving discrete problems. In this paper, we extend the initial sets of populations driven by different perspective-taking inspirations, seeking both optimal configuration for solving a number of TSP benchmarks, at the same time constituting a tool for analyzing socio-cognitive features of the individuals involved. The proposed algorithms are compared against classic ACO, and are found to prevail in most of the benchmark functions tested. |
Reggiani, Claudio; Coppens, Sandra; Sekhara, Tayeb; Dimov, Ivan; Pichon, Bruno; Lufin, Nicolas; Addor, Marie Claude; Belligni, Elga Fabia; Digilio, Maria Cristina; Faletra, Flavio; Ferrero, Giovanni Battista; Gerard, Marion; Isidor, Bertrand; Joss, Shelagh; Niel-Bütschi, Florence; Perrone, Maria Dolores; Petit, Florence; Renieri, Alessandra; Romana, Serge; Topa, Alexandra; Vermeesch, Joris Robert; Lenaerts, Tom; Casimir, Georges; Abramowicz, Marc; Bontempi, Gianluca; Vilain, Catheline; Deconinck, Nicolas; Smits, Guillaume Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability. Journal Article Genome medicine, 9 (1), pp. 67, 2017, (DOI: 10.1186/s13073-017-0452-y). @article{info:hdl:2013/258564, title = {Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability.}, author = {Claudio Reggiani and Sandra Coppens and Tayeb Sekhara and Ivan Dimov and Bruno Pichon and Nicolas Lufin and Marie Claude Addor and Elga Fabia Belligni and Maria Cristina Digilio and Flavio Faletra and Giovanni Battista Ferrero and Marion Gerard and Bertrand Isidor and Shelagh Joss and Florence Niel-Bütschi and Maria Dolores Perrone and Florence Petit and Alessandra Renieri and Serge Romana and Alexandra Topa and Joris Robert Vermeesch and Tom Lenaerts and Georges Casimir and Marc Abramowicz and Gianluca Bontempi and Catheline Vilain and Nicolas Deconinck and Guillaume Smits}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/258564/1/PMC5518101.pdf}, year = {2017}, date = {2017-01-01}, journal = {Genome medicine}, volume = {9}, number = {1}, pages = {67}, abstract = {Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders.}, note = {DOI: 10.1186/s13073-017-0452-y}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. |
Cava, Claudia; Colaprico, Antonio; Bertoli, Gloria; Graudenzi, Alex; Silva, Tiago C; Olsen, Catharina; Noushmehr, Houtan; Bontempi, Gianluca; Mauri, Giancarlo; Castiglioni, Isabella SpidermiR: an R/Bioconductor package for integrative analysis with miRNA data Journal Article International journal of molecular sciences, 2017, (DOI: 10.3390/ijms18020274). @article{info:hdl:2013/245105, title = {SpidermiR: an R/Bioconductor package for integrative analysis with miRNA data}, author = {Claudia Cava and Antonio Colaprico and Gloria Bertoli and Alex Graudenzi and Tiago C Silva and Catharina Olsen and Houtan Noushmehr and Gianluca Bontempi and Giancarlo Mauri and Isabella Castiglioni}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/245105}, year = {2017}, date = {2017-01-01}, journal = {International journal of molecular sciences}, abstract = {Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.}, note = {DOI: 10.3390/ijms18020274}, keywords = {}, pubstate = {published}, tppubtype = {article} } Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally. |
Orlando, Gabriele; Raimondi, Daniele; Khanna, T; Lenaerts, Tom; Vranken, Wim F SVM-dependent pairwise HMM: an application to Protein pairwise alignments. Journal Article Bioinformatics, 2017, (DOI: 10.1093/bioinformatics/btx391). @article{info:hdl:2013/254251, title = {SVM-dependent pairwise HMM: an application to Protein pairwise alignments.}, author = {Gabriele Orlando and Daniele Raimondi and T Khanna and Tom Lenaerts and Wim F Vranken}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/254251}, year = {2017}, date = {2017-01-01}, journal = {Bioinformatics}, abstract = {Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions.}, note = {DOI: 10.1093/bioinformatics/btx391}, keywords = {}, pubstate = {published}, tppubtype = {article} } Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. |
Carcillo, Fabrizio; Pozzolo, Andrea Dal; "e, Yann-A; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark Journal Article Information fusion, 2017, (DOI: 10.1016/j.inffus.2017.09.005). @article{info:hdl:2013/258226, title = {SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark}, author = {Fabrizio Carcillo and Andrea Dal Pozzolo and Yann-A{"e}l Le Borgne and Olivier Caelen and Yannis Mazzer and Gianluca Bontempi}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/258226/4/Elsevier_241853.pdf}, year = {2017}, date = {2017-01-01}, journal = {Information fusion}, abstract = {The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.}, note = {DOI: 10.1016/j.inffus.2017.09.005}, keywords = {}, pubstate = {published}, tppubtype = {article} } The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions. |
Martinez-Vaquero, Luis L A; Han, The Anh T A H; Pereira, Luís Moniz; Lenaerts, Tom When agreement-accepting free-riders are a necessary evil for the evolution of cooperation. Journal Article Scientific reports, 7 (1), pp. 2478, 2017, (DOI: 10.1038/s41598-017-02625-z). @article{info:hdl:2013/256610, title = {When agreement-accepting free-riders are a necessary evil for the evolution of cooperation.}, author = {Luis L A Martinez-Vaquero and The Anh T A H Han and Luís Moniz Pereira and Tom Lenaerts}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/256610/1/PMC5449399.pdf}, year = {2017}, date = {2017-01-01}, journal = {Scientific reports}, volume = {7}, number = {1}, pages = {2478}, abstract = {Agreements and commitments have provided a novel mechanism to promote cooperation in social dilemmas in both one-shot and repeated games. Individuals requesting others to commit to cooperate (proposers) incur a cost, while their co-players are not necessarily required to pay any, allowing them to free-ride on the proposal investment cost (acceptors). Although there is a clear complementarity in these behaviours, no dynamic evidence is currently available that proves that they coexist in different forms of commitment creation. Using a stochastic evolutionary model allowing for mixed population states, we identify non-trivial roles of acceptors as well as the importance of intention recognition in commitments. In the one-shot prisoner's dilemma, alliances between proposers and acceptors are necessary to isolate defectors when proposers do not know the acceptance intentions of the others. However, when the intentions are clear beforehand, the proposers can emerge by themselves. In repeated games with noise, the incapacity of proposers and acceptors to set up alliances makes the emergence of the first harder whenever the latter are present. As a result, acceptors will exploit proposers and take over the population when an apology-forgiveness mechanism with too low apology cost is introduced, and hence reduce the overall cooperation level.}, note = {DOI: 10.1038/s41598-017-02625-z}, keywords = {}, pubstate = {published}, tppubtype = {article} } Agreements and commitments have provided a novel mechanism to promote cooperation in social dilemmas in both one-shot and repeated games. Individuals requesting others to commit to cooperate (proposers) incur a cost, while their co-players are not necessarily required to pay any, allowing them to free-ride on the proposal investment cost (acceptors). Although there is a clear complementarity in these behaviours, no dynamic evidence is currently available that proves that they coexist in different forms of commitment creation. Using a stochastic evolutionary model allowing for mixed population states, we identify non-trivial roles of acceptors as well as the importance of intention recognition in commitments. In the one-shot prisoner's dilemma, alliances between proposers and acceptors are necessary to isolate defectors when proposers do not know the acceptance intentions of the others. However, when the intentions are clear beforehand, the proposers can emerge by themselves. In repeated games with noise, the incapacity of proposers and acceptors to set up alliances makes the emergence of the first harder whenever the latter are present. As a result, acceptors will exploit proposers and take over the population when an apology-forgiveness mechanism with too low apology cost is introduced, and hence reduce the overall cooperation level. |
Pereira, Luís Moniz; Lenaerts, Tom; Martinez-Vaquero, Luis L A; Han, The Anh T A H Social manifestation of guilt leads to stable cooperation in multi-agent systems Journal Article Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 3 , pp. 1421-1430, 2017, (Language of publication: en). @article{info:hdl:2013/271395, title = {Social manifestation of guilt leads to stable cooperation in multi-agent systems}, author = {Luís Moniz Pereira and Tom Lenaerts and Luis L A Martinez-Vaquero and The Anh T A H Han}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/271395}, year = {2017}, date = {2017-01-01}, journal = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS}, volume = {3}, pages = {1421-1430}, abstract = {Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit.}, note = {Language of publication: en}, keywords = {}, pubstate = {published}, tppubtype = {article} } Inspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, analytical and numerical methods from evolutionary game theory are employed to identify the conditions for which enhanced cooperation emerges within the context of the iterated prisoners dilemma. Guilt is modelled explicitly as two features, i.e. A counter that keeps track of the number of transgressions and a threshold that dictates when alleviation (through for instance apology and self-punishment) is required for an emotional agent. Such an alleviation introduces an effect on the payoff of the agent experiencing guilt. We show that when the system consists of agents that resolve their guilt without considering the co-player's attitude towards guilt alleviation then cooperation does not emerge. In that case those guilt prone agents are easily dominated by agents expressing no guilt or having no incentive to alleviate the guilt they experience. When, on the other hand, the guilt prone focal agent requires that guilt only needs to be alleviated when guilt alleviation is also manifested by a defecting co-player, then cooperation may thrive. This observation remains consistent for a generalised model as is discussed in this article. In summary, our analysis provides important insights into the design of multi-agent and cognitive agent systems where the inclusion of guilt modelling can improve agents' cooperative behaviour and overall benefit. |
Jeschke, Jana; Bizet, Martin; Desmedt, Christine; Calonne, Emilie; Dedeurwaerder, Sarah; Garaud, Soizic; Koch, Alexander K; Larsimont, Denis; Salgado, Roberto; den Eynden, Gert Van; Willard-Gallo, Karen; Bontempi, Gianluca; Defrance, Matthieu; Sotiriou, Christos; cc, Fran DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. Journal Article The Journal of clinical investigation, 127 (8), pp. 3090-3102, 2017, (DOI: 10.1172/JCI91095). @article{info:hdl:2013/265312, title = {DNA methylation-based immune response signature improves patient diagnosis in multiple cancers.}, author = {Jana Jeschke and Martin Bizet and Christine Desmedt and Emilie Calonne and Sarah Dedeurwaerder and Soizic Garaud and Alexander K Koch and Denis Larsimont and Roberto Salgado and Gert Van den Eynden and Karen Willard-Gallo and Gianluca Bontempi and Matthieu Defrance and Christos Sotiriou and Fran{cc}ois Fuks}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/265312/1/PMC5531413.pdf}, year = {2017}, date = {2017-01-01}, journal = {The Journal of clinical investigation}, volume = {127}, number = {8}, pages = {3090-3102}, abstract = {The tumor immune response is increasingly associated with better clinical outcomes in breast and other cancers. However, the evaluation of tumor-infiltrating lymphocytes (TILs) relies on histopathological measurements with limited accuracy and reproducibility. Here, we profiled DNA methylation markers to identify a methylation of TIL (MeTIL) signature that recapitulates TIL evaluations and their prognostic value for long-term outcomes in breast cancer (BC).}, note = {DOI: 10.1172/JCI91095}, keywords = {}, pubstate = {published}, tppubtype = {article} } The tumor immune response is increasingly associated with better clinical outcomes in breast and other cancers. However, the evaluation of tumor-infiltrating lymphocytes (TILs) relies on histopathological measurements with limited accuracy and reproducibility. Here, we profiled DNA methylation markers to identify a methylation of TIL (MeTIL) signature that recapitulates TIL evaluations and their prognostic value for long-term outcomes in breast cancer (BC). |
Reggiani, Claudio; Coppens, Sandra; Sekhara, Tayeb; Dimov, Ivan; Pichon, Bruno; Lufin, Nicolas; Addor, Marie Claude; Belligni, Elga Fabia; Digilio, Maria Cristina; Faletra, Flavio; Ferrero, Giovanni Battista; Gerard, Marion; Isidor, Bertrand; Joss, Shelagh; Niel-Bütschi, Florence; Perrone, Maria Dolores; Petit, Florence; Renieri, Alessandra; Romana, Serge; Topa, Alexandra; Vermeesch, Joris Robert; Lenaerts, Tom; Casimir, Georges; Abramowicz, Marc; Bontempi, Gianluca; Vilain, Catheline; Deconinck, Nicolas; Smits, Guillaume Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability. Journal Article Genome medicine, 9 (1), pp. 67, 2017, (DOI: 10.1186/s13073-017-0452-y). @article{info:hdl:2013/258564b, title = {Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability.}, author = {Claudio Reggiani and Sandra Coppens and Tayeb Sekhara and Ivan Dimov and Bruno Pichon and Nicolas Lufin and Marie Claude Addor and Elga Fabia Belligni and Maria Cristina Digilio and Flavio Faletra and Giovanni Battista Ferrero and Marion Gerard and Bertrand Isidor and Shelagh Joss and Florence Niel-Bütschi and Maria Dolores Perrone and Florence Petit and Alessandra Renieri and Serge Romana and Alexandra Topa and Joris Robert Vermeesch and Tom Lenaerts and Georges Casimir and Marc Abramowicz and Gianluca Bontempi and Catheline Vilain and Nicolas Deconinck and Guillaume Smits}, url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/258564/1/PMC5518101.pdf}, year = {2017}, date = {2017-01-01}, journal = {Genome medicine}, volume = {9}, number = {1}, pages = {67}, abstract = {Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders.}, note = {DOI: 10.1186/s13073-017-0452-y}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. |
Pham, Ngoc Cam; Haibe-Kains, Benjamin; Bellot, Pau; Bontempi, Gianluca; Meyer, Patrick E Study of Meta-analysis strategies for network inference using information-theoretic approaches Journal Article BioData Mining, 10 (1), 2017, (DOI: 10.1186/s13040-017-0136-6). @article{info:hdl:2013/259607, title = {Study of Meta-analysis strategies for network inference using information-theoretic approaches}, author = {Ngoc Cam Pham and Benjamin Haibe-Kains and Pau Bellot and Gianluca Bontempi and Patrick E Meyer}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/259607}, year = {2017}, date = {2017-01-01}, journal = {BioData Mining}, volume = {10}, number = {1}, abstract = {Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one (“data merging”) merges all datasets together and then infers a GRN whereas the other (“networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones.}, note = {DOI: 10.1186/s13040-017-0136-6}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one (“data merging”) merges all datasets together and then infers a GRN whereas the other (“networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones. |
Pham, Ngoc Cam; Haibe-Kains, Benjamin; Bellot, Pau; Bontempi, Gianluca; Meyer, Patrick E Study of Meta-analysis Strategies for Network Inference Using Information-Theoretic Approaches Journal Article Proceedings - International Workshop on Database and Expert Systems Applications, pp. 76-83, 2017, (DOI: 10.1109/DEXA.2016.030). @article{info:hdl:2013/247710, title = {Study of Meta-analysis Strategies for Network Inference Using Information-Theoretic Approaches}, author = {Ngoc Cam Pham and Benjamin Haibe-Kains and Pau Bellot and Gianluca Bontempi and Patrick E Meyer}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/247710}, year = {2017}, date = {2017-01-01}, journal = {Proceedings - International Workshop on Database and Expert Systems Applications}, pages = {76-83}, abstract = {Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge insystems biology. Thanks to high-throughput technologies, amassive amount of gene-expression data has been accumulatedin the public repositories. Modelling GRNs from multipleexperiments (also called integrative analysis) has, therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robustthan the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a smallnumber of samples. To date, there are mainly two strategies for the problemof interest: the first one ('data merging') merges all datasetstogether and then infers a GRN whereas the other ('networksensemble') infers GRNs from every dataset separately and thenaggregates them using some ensemble rules (such as ranksumor weightsum). Unfortunately, a thorough comparison of thesetwo approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set ofexperiments based on in silico benchmarks. Furthermore, wepresent a new meta-analysis approach for inferring GRNs frommultiple studies. Our proposed approach, adapted to methodsbased on pairwise measures such as correlation or mutualinformation, consists of two steps: aggregating matrices of thepairwise measures from every dataset followed by extractingthe network from the meta-matrix.}, note = {DOI: 10.1109/DEXA.2016.030}, keywords = {}, pubstate = {published}, tppubtype = {article} } Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge insystems biology. Thanks to high-throughput technologies, amassive amount of gene-expression data has been accumulatedin the public repositories. Modelling GRNs from multipleexperiments (also called integrative analysis) has, therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robustthan the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a smallnumber of samples. To date, there are mainly two strategies for the problemof interest: the first one ('data merging') merges all datasetstogether and then infers a GRN whereas the other ('networksensemble') infers GRNs from every dataset separately and thenaggregates them using some ensemble rules (such as ranksumor weightsum). Unfortunately, a thorough comparison of thesetwo approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set ofexperiments based on in silico benchmarks. Furthermore, wepresent a new meta-analysis approach for inferring GRNs frommultiple studies. Our proposed approach, adapted to methodsbased on pairwise measures such as correlation or mutualinformation, consists of two steps: aggregating matrices of thepairwise measures from every dataset followed by extractingthe network from the meta-matrix. |
Pozzolo, Andrea Dal; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy Journal Article IEEE Transactions on Neural Networks and Learning Systems, 99 , 2017, (DOI: 10.1109/TNNLS.2017.2736643). @article{info:hdl:2013/258224, title = {Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy}, author = {Andrea Dal Pozzolo and Giacomo Boracchi and Olivier Caelen and Cesare Alippi and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/258224}, year = {2017}, date = {2017-01-01}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, volume = {99}, abstract = {Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.}, note = {DOI: 10.1109/TNNLS.2017.2736643}, keywords = {}, pubstate = {published}, tppubtype = {article} } Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years. |
Xu, Taosheng; Le, Thuc Duy; Liu, Lin; Su, Ning; Wang, Rujing; Sun, Bingyu; Colaprico, Antonio; Bontempi, Gianluca; Li, Jiuyong CancerSubtypes: An R/Bioconductor package for molecular cancer subtype identification, validation and visualization Journal Article Bioinformatics, 33 (19), pp. 3131-3133, 2017, (DOI: 10.1093/bioinformatics/btx378). @article{info:hdl:2013/260704, title = {CancerSubtypes: An R/Bioconductor package for molecular cancer subtype identification, validation and visualization}, author = {Taosheng Xu and Thuc Duy Le and Lin Liu and Ning Su and Rujing Wang and Bingyu Sun and Antonio Colaprico and Gianluca Bontempi and Jiuyong Li}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/260704}, year = {2017}, date = {2017-01-01}, journal = {Bioinformatics}, volume = {33}, number = {19}, pages = {3131-3133}, abstract = {Summary Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in theSupplementary Material.}, note = {DOI: 10.1093/bioinformatics/btx378}, keywords = {}, pubstate = {published}, tppubtype = {article} } Summary Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in theSupplementary Material. |
`e, Nathaniel Mon P; Lenaerts, Tom; Pacheco, Jorge M J M; Dingli, David Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria Miscellaneous 2017, (Conference: Benelux Bioinformatics Conference 2017). @misc{info:hdl:2013/267368b, title = {Evolutionary Dynamics of Paroxysmal Nocturnal Hemoglobinuria}, author = {Nathaniel Mon P{`e}re and Tom Lenaerts and Jorge M J M Pacheco and David Dingli}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/267368}, year = {2017}, date = {2017-01-01}, note = {Conference: Benelux Bioinformatics Conference 2017}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Stefani, Jacopo De; Caelen, Olivier; Hattab, Dalila; Bontempi, Gianluca Machine learning for multi-step ahead forecasting of volatility proxies Journal Article CEUR Workshop Proceedings, 1941 , pp. 17-28, 2017, (Language of publication: en). @article{info:hdl:2013/261790, title = {Machine learning for multi-step ahead forecasting of volatility proxies}, author = {Jacopo De Stefani and Olivier Caelen and Dalila Hattab and Gianluca Bontempi}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/261790}, year = {2017}, date = {2017-01-01}, journal = {CEUR Workshop Proceedings}, volume = {1941}, pages = {17-28}, abstract = {In finance, volatility is defined as a measure of variation of a trading price series over time. As volatility is a latent variable, several measures, named proxies, have been proposed in the literature to represent such quantity. The purpose of our work is twofold. On one hand, we aim to perform a statistical assessment of the relationships among the most used proxies in the volatility literature. On the other hand, while the majority of the reviewed studies in the literature focuses on a univariate time series model (NAR), using a single proxy, we propose here a NARX model, combining two proxies to predict one of them, showing that it is possible to improve the prediction of the future value of some proxies by using the information provided by the others. Our results, employing artificial neural networks (ANN), k-Nearest Neighbours (kNN) and support vector regression (SVR), show that the supplementary information carried by the additional proxy could be used to reduce the forecasting error of the aforementioned methods. We conclude by explaining how we wish to further investigate such relationship.}, note = {Language of publication: en}, keywords = {}, pubstate = {published}, tppubtype = {article} } In finance, volatility is defined as a measure of variation of a trading price series over time. As volatility is a latent variable, several measures, named proxies, have been proposed in the literature to represent such quantity. The purpose of our work is twofold. On one hand, we aim to perform a statistical assessment of the relationships among the most used proxies in the volatility literature. On the other hand, while the majority of the reviewed studies in the literature focuses on a univariate time series model (NAR), using a single proxy, we propose here a NARX model, combining two proxies to predict one of them, showing that it is possible to improve the prediction of the future value of some proxies by using the information provided by the others. Our results, employing artificial neural networks (ANN), k-Nearest Neighbours (kNN) and support vector regression (SVR), show that the supplementary information carried by the additional proxy could be used to reduce the forecasting error of the aforementioned methods. We conclude by explaining how we wish to further investigate such relationship. |
Jansen, Maarten Optimisation bias correction in sparse structured variable selection Miscellaneous 2017, (Conference: European Meeting of Statisticians (2017: Helsinki)). @misc{info:hdl:2013/280734, title = {Optimisation bias correction in sparse structured variable selection}, author = {Maarten Jansen}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/280734}, year = {2017}, date = {2017-01-01}, note = {Conference: European Meeting of Statisticians (2017: Helsinki)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Grujić, Jelena; Lenaerts, Tom Looking for the strategies in the repeated prisoner's dilemma when the cooperation is established Miscellaneous 2017, (Conference: 3rd International Conference of Computational Social Science(07/2017: Cologne, Germany)). @misc{info:hdl:2013/263490b, title = {Looking for the strategies in the repeated prisoner's dilemma when the cooperation is established}, author = {Jelena Grujić and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263490}, year = {2017}, date = {2017-01-01}, note = {Conference: 3rd International Conference of Computational Social Science(07/2017: Cologne, Germany)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Gazzo, Andrea; Raimondi, Daniele; Daneels, Dorien; Moreau, Yves; Smits, Guillaume; Dooren, Sonia Van; Lenaerts, Tom Understanding mutational effects in digenic diseases Miscellaneous 2017, (Conference: (21-25 juillet 2017: Prague, Tch`eque)). @misc{info:hdl:2013/263493b, title = {Understanding mutational effects in digenic diseases}, author = {Andrea Gazzo and Daniele Raimondi and Dorien Daneels and Yves Moreau and Guillaume Smits and Sonia Van Dooren and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263493}, year = {2017}, date = {2017-01-01}, note = {Conference: (21-25 juillet 2017: Prague, Tch`eque)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Raimondi, Daniele; cc, Ibrahim Tanyal; Ferte, Julien; Gazzo, Andrea; Orlando, Gabriele; Lenaerts, Tom; Rooman, Marianne; Vranken, Wim F DEOGEN2: prediction and interactive visualisation of SingleAmino Acid Variant deleteriousness in human proteins Miscellaneous 2017, (Conference: (21-25 Juillet 2017: Prague, Tch`eque)). @misc{info:hdl:2013/263494b, title = {DEOGEN2: prediction and interactive visualisation of SingleAmino Acid Variant deleteriousness in human proteins}, author = {Daniele Raimondi and Ibrahim Tanyal{cc}in and Julien Ferte and Andrea Gazzo and Gabriele Orlando and Tom Lenaerts and Marianne Rooman and Wim F Vranken}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263494}, year = {2017}, date = {2017-01-01}, note = {Conference: (21-25 Juillet 2017: Prague, Tch`eque)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Grujić, Jelena; Lenaerts, Tom Network influence on promotion of cooperation - Is there imitation? Miscellaneous 2017, (Conference: 8th Conference on Complex Networks.(03/2017: Dubrovnik, Croatia)). @misc{info:hdl:2013/263489b, title = {Network influence on promotion of cooperation - Is there imitation?}, author = {Jelena Grujić and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263489}, year = {2017}, date = {2017-01-01}, note = {Conference: 8th Conference on Complex Networks.(03/2017: Dubrovnik, Croatia)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Smits, Guillaume; Gazzo, Andrea; Daneels, Dorien; Raimondi, Daniele; Papadimitriou, Sofia; Moreau, Yves; Dooren, Sonia Van; Lenaerts, Tom Understanding combinatorial effects of variants using machine learning and DIDA, the DIgenic diseases Database Miscellaneous 2017, (Conference: Genomics on Rare Disease(Wellcome Genome Campus, Hinxton, Cambridge, UK)). @misc{info:hdl:2013/263492b, title = {Understanding combinatorial effects of variants using machine learning and DIDA, the DIgenic diseases Database}, author = {Guillaume Smits and Andrea Gazzo and Dorien Daneels and Daniele Raimondi and Sofia Papadimitriou and Yves Moreau and Sonia Van Dooren and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263492}, year = {2017}, date = {2017-01-01}, note = {Conference: Genomics on Rare Disease(Wellcome Genome Campus, Hinxton, Cambridge, UK)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |
Fernandez-Domingos, Elias; Burguillo-Rial, Juan C; Lenaerts, Tom Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game Miscellaneous 2017, (Conference: 29nd Benelux conference on Artificial Intelligence(28-29 nov 2017: Groningen)). @misc{info:hdl:2013/263491b, title = {Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game}, author = {Elias Fernandez-Domingos and Juan C Burguillo-Rial and Tom Lenaerts}, url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/263491}, year = {2017}, date = {2017-01-01}, note = {Conference: 29nd Benelux conference on Artificial Intelligence(28-29 nov 2017: Groningen)}, keywords = {}, pubstate = {published}, tppubtype = {misc} } |