2023 |
Coelho, Léonardo Bertolucci; Morillo, Daniel Torres; Bernal, Miguel; Paldino, Gian Marco; Bontempi, Gianluca; Troyano, Jon Ustarroz Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution Journal Article In: Corrosion science, vol. 217, pp. 111104, 2023, (DOI: 10.1016/j.corsci.2023.111104). @article{info:hdl:2013/358502,This investigation proposes using Scanning Electrochemical Cell Microscopy (SECCM) as a high throughput tool to collect corrosion activity from randomly probed locations on 316 L SS. In the presence of chloride, potentiodynamic polarisation tests triggered the development of pitting corrosion. Data science methods were deployed to handle and explore 955 j Vs E curves. Normality tests and fitting with theoretical functions were used to understand the conditional log(j) distributions at different potentials. Unimodal and uniform distributions were assigned to the passive and pitting regions. Our “big-data” local strategy revealed a potential-dependent distribution of log(j), with the randomness increasing with testing aggressiveness. Data availability: All data generated or analysed during this study are included in this published article (and its supplementary information files) and are available in the Mendeley Data repository, [https://data.mendeley.com/datasets/78rz8vw46x/2]. Code availability: The code required to reproduce these findings is included in this published article (and its supplementary information files) and is available to download from GitHub: https://github.com/bcoelho-leonardo/Data-driven-analysis-of-the-local-current-distributions-of-316L-corrosion-in-NaCl-solution/blob/4efff485b115468840b25ea56ad81b31711c0f51/local%20current%20distributions%20of%20316L%20corrosion.ipynb. |
Verhelst, Theo; Mercier, Denis; Shrestha, Jeevan; Bontempi, Gianluca Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment Journal Article In: Machine learning, 2023, (DOI: 10.1007/s10994-023-06317-w). @article{info:hdl:2013/367639,Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the probability of counterfactuals and we show that tightness of such bounds depends on the information of the feature set on the uplift term. Then, we propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes. The quality of the bounds and the point estimators are assessed on synthetic data and a large real-world customer data set provided by a telecom company, showing significant improvement over the state of the art. |
Lunghi, Daniele; Paldino, Gian Marco; Caelen, Olivier; Bontempi, Gianluca An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection Journal Article In: IEEE access, vol. 11, pp. 136666-136679, 2023, (DOI: 10.1109/ACCESS.2023.3337635). @article{info:hdl:2013/367638,Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest. This paper addresses the issue of imbalanced learning in credit card fraud detection by introducing a novel approach that models fraudulent behavior as a time-dependent process. The main contribution is the design and assessment of an oversampling strategy, called ‘Adversary-based Oversampling’ (ADVO), which relies on modeling the temporal relationship among frauds. The strategy is implemented by two learning approaches: first, an innovative regression-based oversampling model that predicts subsequent fraudulent activities based on previous fraud features. Second, the adaptation of the state-of-the-art TimeGAN oversampling algorithm to the context of credit card fraud detection. This adaptation involves treating a sequence of frauds from the same card as a time series, from which artificial frauds’ time series are generated. Experiments have been conducted using real credit card transaction data from our industrial partner, Worldline S.A, and a synthetic dataset generated by a transaction simulator for reproducibility purposes. Our findings show that an oversampling approach incorporating time-dependent modeling of frauds provides competitive results, measured against common fraud detection metrics, compared to traditional oversampling algorithms. |
Claeskens, G.; Jansen, Maarten Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models Journal Article In: Test, vol. 32, no. 4, pp. 1177-1179, 2023, (DOI: 10.1007/s11749-023-00896-5). @article{info:hdl:2013/371479, |
Claeskens, G.; Jansen, Maarten; Zhou, Jing Discussion on: “A scale-free approach for false discovery rate control in generalized linear models” by Dai, Lin, Zing, Liu. Journal Article In: Journal of the American Statistical Association, vol. 118, no. 543, pp. 1573-1577, 2023, (Language of publication: fr). @article{info:hdl:2013/359639, |
Bhattacharya, Shreya; Lefèvre, Laure; Hayakawa, Hisashi; Jansen, Maarten; Clette, Frédéric L. Scale Transfer in 1849: Heinrich Schwabe to Rudolf Wolf Journal Article In: Solar physics, vol. 298, no. 1, pp. 1-12, 2023, (Language of publication: fr). @article{info:hdl:2013/359132b, |
2022 |
Piron, Anthony; Szymczak, Florian; Alvelos, Maria De Oliveira; Defrance, Matthieu; Lenaerts, Tom; Eizirik, Decio L.; Cnop, Miriam RedRibbon: A new rank-rank hypergeometric overlap pipeline to compare gene and transcript expression signatures Journal Article In: BioRxiv, 2022, (DOI: https://doi.org/10.1101/2022.08.31.505818). @article{info:hdl:2013/353212d,Motivation. High throughput omics technologies have generated a wealth of large protein, gene and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at both gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers.Results. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anti-correlation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. Additionally, the package is a drop-in replacement to previous packages as a compatibility mode is included, allowing to re-run older studies with close to no change to existing pipelines. RedRibbon holds the promise to accurately extricate detailed information from large analyses.Availability. RNA-sequencing datasets are available through the Gene Expression Omnibus (GEO) portal with accession numbers GSE159984, GSE133218, GSE137136, GSE98485, GSE148058 and GSE108413. The C libraries and R package code are open to the community with a permissive licence (GPL3) and available for download from GitHub https://github.com/antpiron/ale, https://github.com/antpiron/cRedRibbon and https://github.com/antpiron/RedRibbon. |
Grolaux, Robin; Hardy, Alexis; Olsen, Catharina; Dooren, Sonia Van; Smits, Guillaume; Defrance, Matthieu Identification of differentially methylated regions in rare diseases from a single-patient perspective Journal Article In: Clinical Epigenetics, vol. 14, no. 1, 2022, (DOI: 10.1186/s13148-022-01403-7). @article{info:hdl:2013/353081,Abstract Background DNA methylation (5-mC) is being widely recognized as an alternative in the detection of sequence variants in the diagnosis of some rare neurodevelopmental and imprinting disorders. Identification of alterations in DNA methylation plays an important role in the diagnosis and understanding of the etiology of those disorders. Canonical pipelines for the detection of differentially methylated regions (DMRs) usually rely on inter-group (e.g., case versus control) comparisons. However, these tools might perform suboptimally in the context of rare diseases and multilocus imprinting disturbances due to small cohort sizes and inter-patient heterogeneity. Therefore, there is a need to provide a simple but statistically robust pipeline for scientists and clinicians to perform differential methylation analyses at the single patient level as well as to evaluate how parameter fine-tuning may affect differentially methylated region detection. Result We implemented an improved statistical method to detect differentially methylated regions in correlated datasets based on the Z-score and empirical Brown aggregation methods from a single-patient perspective. To accurately assess the predictive power of our method, we generated semi-simulated data using a public control population of 521 samples and investigated how the size of the control population, methylation difference, and region size affect DMR detection. In addition, we validated the detection of methylation events in patients suffering from rare multi-locus imprinting disturbance and evaluated how this method could complement existing tools in the context of clinical diagnosis. Conclusion In this study, we present a robust statistical method to perform differential methylation analysis at the single patient level and describe its optimal parameters to increase DMRs identification performance. Finally, we show its diagnostic utility when applied to rare disorders. |
Bizet, Martin; Defrance, Matthieu; Calonne, Emilie; Bontempi, Gianluca; Sotiriou, Christos; Fuks, Franccois; Jeschke, Jana In: Epigenetics, vol. 17, no. 13, pp. 2434-2454, 2022, (DOI: 10.1080/15592294.2022.2135201). @article{info:hdl:2013/353467b,Illumina Infinium DNA Methylation (5mC) arrays are a popular technology for low-cost, high-throughput, genome-scale measurement of 5mC distribution, especially in cancer and other complex diseases. After the success of its HumanMethylation450 array (450k), Illumina released the MethylationEPIC array (850k) featuring increased coverage of enhancers. Despite the widespread use of 850k, analysis of the corresponding data remains suboptimal: it still relies mostly on Illumina’s default annotation, which underestimates enhancerss and long noncoding RNAs. Results: We have thus developed an approach, based on the ENCODE and LNCipedia databases, which greatly improves upon Illumina’s default annotation of enhancers and long noncoding transcripts. We compared the re-annotated 850k with both 450k and reduced-representation bisulphite sequencing (RRBS), another high-throughput 5mC profiling technology. We found 850k to cover at least three times as many enhancers and long noncoding RNAs as either 450k or RRBS. We further investigated the reproducibility of the three technologies, applying various normalization methods to the 850k data. Most of these methods reduced variability to a level below that of RRBS data. We then used 850k with our new annotation and normalization to profile 5mC changes in breast cancer biopsies. 850k highlighted aberrant enhancer methylation as the predominant feature, in agreement with previous reports. Our study provides an updated processing approach for 850k data, based on refined probe annotation and normalization, allowing for improved analysis of methylation at enhancers and long noncoding RNA genes. Our findings will help to further advance understanding of the DNA methylome in health and disease. |
Rivière, Quentin; Corso, Massimiliano; Ciortan, Madalina; Noël, Grégoire; Verbruggen, Nathalie; Defrance, Matthieu Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants. Journal Article In: Plant and Cell Physiology, vol. 63, no. 10, pp. 1457-1473, 2022, (DOI: 10.1093/pcp/pcac095). @article{info:hdl:2013/352290,The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays. |
Ciortan, Madalina; Defrance, Matthieu GNN-based embedding for clustering scRNA-seq data Journal Article In: Bioinformatics, vol. 38, no. 4, pp. 1037-1044, 2022, (DOI: 10.1093/bioinformatics/btab787). @article{info:hdl:2013/343811b,Abstract Motivation Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analysis is usually used to predict cell class assignments and infer cell identities. However, the high sparsity of scRNA-seq data, accentuated by dropout events generates challenges that have motivated the development of numerous dedicated clustering methods. Nevertheless, there is still no consensus on the best performing method. Results graph-sc is a new method leveraging a graph autoencoder network to create embeddings for scRNA-seq cell data. While this work analyzes the performance of clustering the embeddings with various clustering algorithms, other downstream tasks can also be performed. A broad experimental study has been performed on both simulated and scRNA-seq datasets. The results indicate that although there is no consistently best method across all the analyzed datasets, graph-sc compares favorably to competing techniques across all types of datasets. Furthermore, the proposed method is stable across consecutive runs, robust to input down-sampling, generally insensitive to changes in the network architecture or training parameters and more computationally efficient than other competing methods based on neural networks. Modeling the data as a graph provides increased flexibility to define custom features characterizing the genes, the cells and their interactions. Moreover, external data (e.g. gene network) can easily be integrated into the graph and used seamlessly under the same optimization task. Availability and implementation https://github.com/ciortanmadalina/graph-sc. Supplementary information Supplementary data are available at Bioinformatics online. |
Renaux, Alexandre; Terwagne, Chloé CT; Cochez, Michael; Tiddi, Ilaria; Nowé, Ann; Lenaerts, Tom A knowledge graph approach for interpretable prediction of pathogenic genetic interactions Miscellaneous 2022, (Conference: European Conference on Computational Biology (ECCB) 2022 (2022-07: Sitges, Spain)). @misc{info:hdl:2013/352608,An increasing number of clinical studies are reporting patterns of oligogenic inheritance in genetic diseases. Despite the advent of methods able to predict the pathogenicity of variant combinations, the underlying biological mechanisms remain unknown, since these models offer limited interpretability. To advance towards a better understanding of oligogenic disease aetiology, we developed a new interpretable predictive method based on a knowledge graph. This heterogenous network integrates curated oligogenic combinations together with multiple biological networks and biomedical ontologies. Our approach successfully captures association rules solely based on multi-hop relationships between genes. It combines them as a decision set model which can predict the pathogenicity of new gene pairs. These predictions come with explanations, obtained by querying the knowledge graph, which highlight relevant paths. The benchmarking of this model in a cross-validation setting achieves high accuracy and recalls independent gene pairs from recently published digenic combinations. The analysis of the rule-based paths highlights relevant contributors to the disease and shows the ability of this approach to generate knowledge-based hypotheses to investigate new disease mechanisms. |
Abels, Axel; Lenaerts, Tom; Trianni, Vito; Nowé, Ann A New Approach to Handle Non-Stationarity in Collective Decision-Making Miscellaneous 2022, (Conference: ACM Collective Intelligence conference (CI)(Virtual)). @misc{info:hdl:2013/366666, |
Montero-Porras, Eladio; Gruji’c, Jelena; Domingos, Elias Fernandez; Lenaerts, Tom Inferring Strategies from Observations in Long Iterated Prisoner’s Dilemma Experiments Miscellaneous 2022, (Conference: Complex Systems Conference 2022(17-21/10/2022: Palma de Mallorca, Spain)). @misc{info:hdl:2013/366678, |
Versbraegen, Nassim; Gravel, Barbara; Nachtegael, Charlotte; Renaux, Alexandre; Verkinderen, Emma; Nowé, Ann; Lenaerts, Tom; Papadimitriou, Sofia Taking the prediction of pathogenic variant-combinations to the next level with VarCoPP2.0 Miscellaneous 2022, (Conference: European Conference on Computational Biology (21: 12-21 September 2022: Sitges, Barcelona)). @misc{info:hdl:2013/352566, |
Papadimitriou, Sofia; Gravel, Barbara; Nachtegael, Charlotte; Baere, Elfride De; Loeys, Bart; Vikkula, Miikka; Smits, Guillaume; Lenaerts, Tom 2022, (Conference: Rare Med Symposium(8-12-2022: Gent)). @misc{info:hdl:2013/366742,Background/Aims:Reports of oligogenic cases (i.e. individuals whose disease phenotype can only be explained by the co-occurrence of multiple variants in several genes) have been rapidly increasing, in an effort to close the gap of missing genetic diagnoses. Nevertheless, the quality of this data had never been properly assessed, especially as standards and guidelines for such cases are currently missing. This work, aimed to collect all reported oligogenic cases in one database, OLIDA, assess the quality of the reported information and provide, for the first time, recommendations for their proper reporting. Methods:318 research articles reporting oligogenic cases were extracted from PubMed. Independent curators collected the relevant oligogenic information (i) from the articles and (ii) from public relevant databases. With this data, a transparent curation protocol was developed assigning a confidence score to each oligogenic case based on the amount of pathogenic evidence at the genetic and functional level. The collection and assessment of this data led to the creation of OLIDA, the Oligogenic Diseases Database. Results:OLIDA contains information on oligogenic cases linked to 177 different genetic diseases. Each instance is linked with a confidence score depicting the quality of the associated genetic and functional pathogenic evidence. The data revealed that the majority of papers do not provide proper genetic evidence excluding a monogenic model, while this evidence is rarely coupled with functional experiments for confirmation. Our recommendations stress the necessity of fulfilling both conditions. The use of multiple extended pedigrees showing a clear segregation of the reported variants, control cohorts of a suitable size, as well as functional experiments showing the synergistic effect of the involved variants are essential for this purpose. Conclusion:With our work we reveal the recurrent issues on the reporting of oligogenic cases and stress the need for the development of standards in the field. As the number of papers identifying oligogenic causes to disease is increasing rapidly, initiating this discussion is imperative. |
Abels, Axel; Domingos, Elias Fernandez; Lenaerts, Tom; Trianni, Vito; Nowé, Ann Bias Mitigation in Decision-Making with Expert Advice Miscellaneous 2022, (Conference: Benelux AI Conference (BNAIC) and Benelux machine learning conference (Benelearn)(7-9/11/2022: Antwerpen, Belgique)). @misc{info:hdl:2013/366668b, |
Abels, Axel; Lenaerts, Tom; Trianni, Vito; Nowé, Ann A Novel Approach to Handle Non-stationarity in Collective Decision-Making with Experts Miscellaneous 2022, (Conference: ACM Collective Intelligence Conference 2022(20-21 Octobre 2022: Online)). @misc{info:hdl:2013/352851b, |
Piron, Anthony; Colli, Maikel Luis; Defrance, Matthieu; Eizirik, Decio L.; Mercader, Josep Maria; Cnop, Miriam Identification of novel type 1 and type 2 diabetes genes by colocalisation of human islet eQTL and GWAS variants Miscellaneous 2022, (Conference: EASD Annual Meeting of the European Association for the Study of Diabetes(58th: 19 – 23 September 2022: Stockholm, Sweden)). @misc{info:hdl:2013/353214, |
Terrucha, Ines; Domingos, Elias Fernandez; Santos, Francisco C; Simoens, Pieter; Lenaerts, Tom The art of compensation : how hybrid teams solve collective risk dilemmas Miscellaneous 2022, (Conference: Adaptive and Learning Agents (ALA) Workshop(9-10/5/2022: Auckland, NZ)). @misc{info:hdl:2013/366661, |
Montero-Porras, Eladio; Lenaerts, Tom; Gallotti, Riccardo; Gruji’c, Jelena Fast deliberation is related to unconditional behaviour in iterated Prisoners’ Dilemma experiments Journal Article In: Scientific Reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-24849-4). @article{info:hdl:2013/366631,Abstract People have different preferences for what they allocate for themselves and what they allocate to others in social dilemmas. These differences result from contextual reasons, intrinsic values, and social expectations. What is still an area of debate is whether these differences can be estimated from differences in each individual’s deliberation process. In this work, we analyse the participants’ reaction times in three different experiments of the Iterated Prisoner’s Dilemma with the Drift Diffusion Model, which links response times to the perceived difficulty of the decision task, the rate of accumulation of information (deliberation), and the intuitive attitudes towards the choices. The correlation between these results and the attitude of the participants towards the allocation of resources is then determined. We observe that individuals who allocated resources equally are correlated with more deliberation than highly cooperative or highly defective participants, who accumulate evidence more quickly to reach a decision. Also, the evidence collection is faster in fixed neighbour settings than in shuffled ones. Consequently, fast decisions do not distinguish cooperators from defectors in these experiments, but appear to separate those that are more reactive to the behaviour of others from those that act categorically. |
Nachtegael, Charlotte; Gravel, Barbara; Dillen, Arnau; Smits, Guillaume; Nowe, Ann; Papadimitriou, Sofia; Lenaerts, Tom Scaling up oligogenic diseases research with OLIDA: The Oligogenic Diseases Database Journal Article In: Database, vol. 2022, 2022, (DOI: 10.1093/database/baac023). @article{info:hdl:2013/342417b,Improving the understanding of the oligogenic nature of diseases requires access to high-quality, well-curated Findable, Accessible, Interoperable, Reusable (FAIR) data. Although first steps were taken with the development of the Digenic Diseases Database, leading to novel computational advancements to assist the field, these were also linked with a number of limitations, for instance, the ad hoc curation protocol and the inclusion of only digenic cases. The OLIgogenic diseases DAtabase (OLIDA) presents a novel, transparent and rigorous curation protocol, introducing a confidence scoring mechanism for the published oligogenic literature. The application of this protocol on the oligogenic literature generated a new repository containing 916 oligogenic variant combinations linked to 159 distinct diseases. Information extracted from the scientific literature is supplemented with current knowledge support obtained from public databases. Each entry is an oligogenic combination linked to a disease, labelled with a confidence score based on the level of genetic and functional evidence that supports its involvement in this disease. These scores allow users to assess the relevance and proof of pathogenicity of each oligogenic combination in the database, constituting markers for reporting improvements on disease-causing oligogenic variant combinations. OLIDA follows the FAIR principles, providing detailed documentation, easy data access through its application programming interface and website, use of unique identifiers and links to existing ontologies. Database URL: https://olida.ibsquare.be |
Domingos, Elias Fernandez; Terrucha, Ines; Suchon, Remi; Grujić, Jelena; Burguillo, Juan J. C.; Santos, Francisco C.; Lenaerts, Tom Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma Journal Article In: Scientific reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-11518-9). @article{info:hdl:2013/349554b,Home assistant chat-bots, self-driving cars, drones or automated negotiation systems are some of the several examples of autonomous (artificial) agents that have pervaded our society. These agents enable the automation of multiple tasks, saving time and (human) effort. However, their presence in social settings raises the need for a better understanding of their effect on social interactions and how they may be used to enhance cooperation towards the public good, instead of hindering it. To this end, we present an experimental study of human delegation to autonomous agents and hybrid human-agent interactions centered on a non-linear public goods dilemma with uncertain returns in which participants face a collective risk. Our aim is to understand experimentally whether the presence of autonomous agents has a positive or negative impact on social behaviour, equality and cooperation in such a dilemma. Our results show that cooperation and group success increases when participants delegate their actions to an artificial agent that plays on their behalf. Yet, this positive effect is less pronounced when humans interact in hybrid human-agent groups, where we mostly observe that humans in successful hybrid groups make higher contributions earlier in the game. Also, we show that participants wrongly believe that artificial agents will contribute less to the collective effort. In general, our results suggest that delegation to autonomous agents has the potential to work as commitment devices, which prevent both the temptation to deviate to an alternate (less collectively good) course of action, as well as limiting responses based on betrayal aversion. |
Han, The Anh T. A. H.; Lenaerts, Tom; Santos, Francisco C.; Pereira, Luís Moniz Voluntary safety commitments provide an escape from over-regulation in AI development Journal Article In: Technology in society, vol. 68, 2022, (DOI: 10.1016/j.techsoc.2021.101843). @article{info:hdl:2013/339040,With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as their inherent biases, incorporated during their creation, have led to a demand for governance and associated regulation. Yet regulating an innovation process that is not well understood may stifle this process and reduce benefits that society may gain from the generated technology, even under the best intentions. Instruments to shed light on such processes are thus needed as they can ensure that imposed policies achieve the ambitions for which they were designed. Starting from a game-theoretical model that captures the fundamental dynamics of a race for domain supremacy using AI technology, we show how socially unwanted outcomes may be produced when sanctioning is applied unconditionally to risk-taking, i.e. potentially unsafe, behaviours. We demonstrate here the potential of a regulatory approach that combines a voluntary commitment approach reminiscent of soft law, wherein technologists have the freedom of choice between independently pursuing their course of actions or establishing binding agreements to act safely, with either a peer or governmental sanctioning system of those that do not abide by what they pledged. As commitments are binding and sanctioned, they go beyond the classic view of soft law, akin more closely to actual law-enforced regulation. Overall, this work reveals how voluntary but sanctionable commitments generate socially beneficial outcomes in all scenarios envisageable in a short-term race towards domain supremacy through AI technology. These results provide an original dynamic systems perspective of the governance potential of enforceable soft law techniques or co-regulatory mechanisms, showing how they may impact the ambitions of developers in the context of the AI-based applications. |
Paldino, Gian Marco; Caro, Fabrizio De; Stefani, Jacopo De; Vaccaro, Alfredo A.; Villacci, Domenico D.; Bontempi, Gianluca A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines Journal Article In: Energies, vol. 15, no. 6, 2022, (DOI: 10.3390/en15062254). @article{info:hdl:2013/342471b,The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest that the Digital Twin provides more accurate and robust estimations, serving as a complement, or a potential alternative, to traditional methods. |
Marquis, Bastien; Jansen, Maarten Information criteria bias correction for group selection Journal Article In: Statistical papers, 2022, (Language of publication: fr). @article{info:hdl:2013/335472, |
Cimpeanu, Theodor; Santos, Francisco C.; Pereira, Luís Marcelo; Lenaerts, Tom; Han, The Anh T. A. H. Artificial intelligence development races in heterogeneous settings Journal Article In: Scientific reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-05729-3). @article{info:hdl:2013/341515, |
Simar, Cédric; Petit, Robin; Bozga, Nichita; Leroy, Axelle; Alvarez, Ana Maria Cebolla; Petieau, Mathieu; Bontempi, Gianluca; Chéron, Guy Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans. Journal Article In: PloS one, vol. 17, no. 1, pp. e0262417, 2022, (DOI: 10.1371/journal.pone.0262417). @article{info:hdl:2013/366038b,Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field. |
Jansen, Maarten Wavelets from a Statistical Perspective Book CRC Press, 2022, (Language of publication: fr). @book{info:hdl:2013/333285, |
2021 |
Bhattacharya, S.; Jansen, Maarten; Lefèvre, Laure; Clette, Frédéric L. Quality Assessment of Sunspot data using different catalogs Miscellaneous 2021, (Conference: Virtual Conference on Applications of Statistical Methods and Machine Learning in the Space Sciences (17-21 May 2021: Space Science Institute, Boulder, Colorado)). @misc{info:hdl:2013/332104c, |
Alonso, Lorena; Piron, Anthony; Morán, Ignasi; Guindo-Martínez, Marta; Bonàs-Guarch, Sílvia; Atla, Goutham; Miguel-Escalada, Irene; Royo, Romina; Puiggròs, Montserrat; Garcia-Hurtado, Xavier; Suleiman, Mara; Marselli, Lorella; Esguerra, Jonathan L S; Turatsinze, Jean Valéry; Torres, Jason M; Nylander, Vibe; Chen, Ji; Eliasson, Lena; Defrance, Matthieu; Amela, Ramon; MAGIC,; Mulder, Hindrik; Gloyn, Anna L; Groop, Leif; Marchetti, Piero; Eizirik, Decio L.; Ferrer, Jorge; Mercader, Josep JM; Cnop, Miriam; Torrents, David TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Journal Article In: Cell reports, vol. 37, no. 2, pp. 109807, 2021, (DOI: 10.1016/j.celrep.2021.109807). @article{info:hdl:2013/334299b,Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D. |
Han, The Anh T. A. H.; Pereira, Luis Moniz; Santos, Francisco C; Lenaerts, Tom Time-scale Differences will Influence the Regulation Required in an Idealised AI Race Game Miscellaneous 2021, (Conference: International Joint Conference on Artificial Intelligence (IJCAI)(30: 19-26/8/2021: Montreal, Canada)). @misc{info:hdl:2013/336167b, |
Han, The Anh T. A. H.; Lenaerts, Tom; Santos, Francisco C; Pereira, Luís Moniz Voluntary safety commitments provide an escape from over-regulation in AI development Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon,France)). @misc{info:hdl:2013/336169b, |
Hardy, Alexis; Matelot, Mélody; Touzeau, Amandine; Klopp, Christophe; Lopez-Roques, Céline; Duharcourt, Sandra; Defrance, Matthieu DNAModAnnot: A R toolbox for DNA modification filtering and annotation Journal Article In: Bioinformatics, vol. 37, no. 17, pp. 2738-2740, 2021, (DOI: 10.1093/bioinformatics/btab032). @article{info:hdl:2013/333678,Motivation: Long-read sequencing technologies can be employed to detect and map DNA modifications at the nucleotide resolution on a genome-wide scale. However, published software packages neglect the integration of genomic annotation and comprehensive filtering when analyzing patterns of modified bases detected using Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data. Here, we present DNA Modification Annotation (DNAModAnnot), a R package designed for the global analysis of DNA modification patterns using adapted filtering and visualization tools. Results: We tested our package using PacBio sequencing data to analyze patterns of the 6-methyladenine (6mA) in the ciliate Paramecium tetraurelia, in which high 6mA amounts were previously reported. We found P. tetraurelia 6mA genome-wide distribution to be similar to other ciliates. We also performed 5-methylcytosine (5mC) analysis in human lymphoblastoid cells using ONT data and confirmed previously known patterns of 5mC. DNAModAnnot provides a toolbox for the genome-wide analysis of different DNA modifications using PacBio and ONT long-read sequencing data. |
Perez, Rémy; Araujo, Natalia Souza; Defrance, Matthieu; Aron, Serge Molecular adaptations to heat stress in the thermophilic ant genus Cataglyphis Journal Article In: Molecular ecology, 2021, (DOI: 10.1111/mec.16134). @article{info:hdl:2013/332290,Over the last decade, increasing attention has been paid to the molecular adaptations used by organisms to cope with thermal stress. However, to date, few studies have focused on thermophilic species living in hot, arid climates. In this study, we explored molecular adaptations to heat stress in the thermophilic ant genus Cataglyphis, one of the world’s most thermotolerant animal taxa. We compared heat tolerance and gene expression patterns across six Cataglyphis species from distinct phylogenetic groups that live in different habitats and experience different thermal regimes. We found that all six species had high heat tolerance levels with critical thermal maxima (CTmax) ranging from 43℃ to 45℃ and a median lethal temperature (LT50) ranging from 44.5℃ to 46.8℃. Transcriptome analyses revealed that, although the number of differentially expressed genes varied widely for the six species (from 54 to 1118), many were also shared. Functional annotation of the differentially expressed and co-expressed genes showed that the biological pathways involved in heat-shock responses were similar among species and were associated with four major processes: the regulation of transcriptional machinery and DNA metabolism; the preservation of proteome stability; the elimination of toxic residues; and the maintenance of cellular integrity. Overall, our results suggest that molecular responses to heat stress have been evolutionarily conserved in the ant genus Cataglyphis and that their diversity may help workers withstand temperatures close to their physiological limits. |
Ciortan, Madalina; Defrance, Matthieu Contrastive self-supervised clustering of scRNA-seq data. Journal Article In: BMC bioinformatics, vol. 22, no. 1, pp. 280, 2021, (DOI: 10.1186/s12859-021-04210-8). @article{info:hdl:2013/325214,Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification. |
Han, The Anh T. A. H.; Pereira, Luis Moniz; Lenaerts, Tom; Santos, Francisco C Mediating Artificial Intelligence Developments through Negative and Positive Incentives Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon, France)). @misc{info:hdl:2013/336166, |
Cimpeanu, Theodor; Han, The Anh T. A. H.; Santos, Francisco C; Pereira, Luis Moniz; Lenaerts, Tom Heterogeneous Interactions in Artificial Intelligence Development Races Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon. France)). @misc{info:hdl:2013/336168, |
Domingos, Elias Fernandez; Grujić, Jelena; Burguillo, Juan Carlos; Santos, Francisco C; Lenaerts, Tom Modeling behavioral experiments on uncertainty and cooperation with population-based reinforcement learning Miscellaneous 2021, (Conference: Artificial Life Conference(19-23/7/2021: Prague, Czech Republic)). @misc{info:hdl:2013/336173, |
Ciortan, Madalina; Defrance, Matthieu Optimization algorithm for omic data subspace clustering Proceedings Article In: CSBio2021: The 12th International Conference on Computational Systems-Biology and Bioinformatics, pp. Pages 69–89, 2021, (Conference: CSBio2021). @inproceedings{info:hdl:2013/366011b, |
Bogaerts, Bart; Bontempi, Gianluca; Geurts, Pierre; Harley, Nicolas; Lebichot, Bertrand; Lenaerts, Tom; Louppe, Gilles 2021, (DOI: 10.1007/978-3-030-65154-1). @book{info:hdl:2013/336059b, |
Bontempi, Gianluca Statistical foundations of machine learning Book 2021, (Language of publication: en). @book{info:hdl:2013/325210c, |
Bontempi, Gianluca; Chavarriaga, Ricardo; Canck, Hans; Girardi, Emanuela; Hoos, Holger H; Kilbane-Dawe, Iarla; Ball, Tonio; Nowe, Ann; Sousa, Jose; Bacciu, Davide; Aldinucci, Marco; Domenico, Manlio; Saffiotti, Alessandro; Maratea, Marco The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article In: Ethics and information technology, 2021, (DOI: 10.1007/s10676-020-09567-7). @article{info:hdl:2013/321123,A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE’s self-organising volunteers delivered the World’s first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises. |
Han, The Anh T. A. H.; Lenaerts, Tom; Santos, Francisco C; Pereira, Luís Moniz Voluntary safety commitments provide an escape from over-regulation in AI development Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon,France)). @misc{info:hdl:2013/336169, |
Walsh, Ian; Fishman, Dmytro; Garcia-Gasulla, Dario; Titma, Tiina; Pollastri, Gianluca; Capriotti, Emidio; Casadio, Rita RC; Capella-Gutierrez, Salvador; Cirillo, Davide; Conte, Alessio Del; Dimopoulos, Alexandros A. C.; Angel, Victoria Dominguez Del; Dopazo, Joaquin; Fariselli, Piero; Fernández, José Maria; Huber, Florian; Kreshuk, Anna; Lenaerts, Tom; Martelli, Pier Luigi; Navarro, Arcadi; Broin, Pilib; Piñero, Janet; Piovesan, Damiano; Reczko, Martin; Ronzano, Francesco; Satagopam, Venkata; Savojardo, Castrense; Spiwok, Vojtěch; Tangaro, Marco Antonio; Tartari, Giacomo; Salgado, David; Valencia, Alfonso; Zambelli, Federico; Harrow, Jennifer; Psomopoulos, Fotis F. E.; Tosatto, Silvio S. C. E. DOME: recommendations for supervised machine learning validation in biology Journal Article In: Nature methods, 2021, (DOI: 10.1038/s41592-021-01205-4). @article{info:hdl:2013/331277,In the version of this Comment initially published, an error appeared in the “Specificity” equation displayed in the middle-right panel of Fig. 2. Originally reading “ fp/fp+tn”, the equation has been corrected to read: “ tn/tn+fp”. The error has been corrected in the online version of the Article. *A list of authors and their affiliations appears online. |
Laan, Maris; Kasak, Laura; Timinskas, Kęstutis; Grigorova, Marina; Venclovas, Česlovas; Renaux, Alexandre; Lenaerts, Tom; Punab, Margus NR5A1 c.991-1G > C splice-site variant causes familial 46,XY partial gonadal dysgenesis with incomplete penetrance Journal Article In: Clinical endocrinology, 2021, (DOI: 10.1111/cen.14381). @article{info:hdl:2013/316657,Objective: The study aimed to identify the genetic basis of partial gonadal dysgenesis (PGD) in a non-consanguineous family from Estonia. Patients: Cousins P (proband) 1 (12 years; 46,XY) and P2 (18 years; 46,XY) presented bilateral cryptorchidism, severe penoscrotal hypospadias, low bitesticular volume and azoospermia in P2. Their distant relative, P3 (30 years; 46,XY), presented bilateral cryptorchidism and cryptozoospermia. Design: Exome sequencing was targeted to P1-P3 and five unaffected family members. Results: P1-P2 were identified as heterozygous carriers of NR5A1 c.991-1G > C. NR5A1 encodes the steroidogenic factor-1 essential in gonadal development and specifically expressed in adrenal, spleen, pituitary and testes. Together with a previous PGD case from Belgium (Robevska et al 2018), c.991-1G > C represents the first recurrent NR5A1 splice-site mutation identified in patients. The majority of previous reports on NR5A1 mutation carriers have not included phenotype-genotype data of the family members. Segregation analysis across three generations showed incomplete penetrance (<50%) and phenotypic variability among the carriers of NR5A1 c.991-1G > C. The variant pathogenicity was possibly modulated by rare heterozygous variants inherited from the other parent, OTX2 p.P134R (P1) or PROP1 c.301_302delAG (P2). For P3, the pedigree structure supported a distinct genetic cause. He carries a previously undescribed likely pathogenic variant SOS1 p.Y136H. SOS1, critical in Ras/MAPK signalling and foetal development, is a strong novel candidate gene for cryptorchidism. Conclusions: Detailed genetic profiling facilitates counselling and clinical management of the probands, and supports unaffected mutation carriers in the family for their reproductive decision making. |
Père, Nathaniel Vincent Mon; Lenaerts, Tom; Pacheco, Jorge Manuel S.; Dingli, David Multistage feedback-driven compartmental dynamics of hematopoiesis Journal Article In: iScience, vol. 24, no. 4, 2021, (DOI: 10.1016/j.isci.2021.102326). @article{info:hdl:2013/322462,Human hematopoiesis is surprisingly resilient to disruptions, providing suitable responses to severe bleeding, long-lasting immune activation, and even bone marrow transplants. Still, many blood disorders exist which push the system past its natural plasticity, resulting in abnormalities in the circulating blood. While proper treatment of such diseases can benefit from understanding the underlying cell dynamics, these are non-trivial to predict due to the hematopoietic system’s hierarchical nature and complex feedback networks. To characterize the dynamics following different types of perturbations, we investigate a model representing hematopoiesis as a sequence of compartments covering all maturation stages—from stem to mature cells—where feedback regulates cell production to ongoing necessities. We find that a stable response to perturbations requires the simultaneous adaptation of cell differentiation and self-renewal rates, and show that under conditions of continuous disruption—as found in chronic hemolytic states—compartment cell numbers evolve to novel stable states. |
Lebichot, Bertrand; Verhelst, Theo; Borgne, Yann-Aël Le; He-Guelton, Liyun; Oblé, Frédéric; Bontempi, Gianluca Transfer Learning Strategies for Credit Card Fraud Detection Journal Article In: IEEE access, vol. 9, pp. 114754-114766, 2021, (DOI: 10.1109/ACCESS.2021.3104472). @article{info:hdl:2013/331622b,Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with short reaction time and high precision. Nevertheless, the heterogeneous nature of the fraud behavior makes it difficult to tailor existing systems to different contexts (e.g. new payment systems, different countries and/or population segments). Given the high cost (research, prototype development, and implementation in production) of designing data-driven FDSs, it is crucial for transactional companies to define procedures able to adapt existing pipelines to new challenges. From an AI/machine learning perspective, this is known as the problem of transfer learning. This paper discusses the design and implementation of transfer learning approaches for e-commerce credit card fraud detection and their assessment in a real setting. The case study, based on a six-month dataset (more than 200 million e-commerce transactions) provided by the industrial partner, relates to the transfer of detection models developed for a European country to another country. In particular, we present and discuss 15 transfer learning techniques (ranging from naive baselines to state-of-the-art and new approaches), making a critical and quantitative comparison in terms of precision for different transfer scenarios. Our contributions are twofold: (i) we show that the accuracy of many transfer methods is strongly dependent on the number of labeled samples in the target domain and (ii) we propose an ensemble solution to this problem based on self-supervised and semi-supervised domain adaptation classifiers. The thorough experimental assessment shows that this solution is both highly accurate and hardly sensitive to the number of labeled samples. |
Verhelst, Theo; Shrestha, Jeevan; Mercier, Denis; Dewitte, Jean Christophe; Bontempi, Gianluca Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention Journal Article In: Lecture notes in computer science, vol. 12986 LNAI, pp. 44-54, 2021, (DOI: 10.1007/978-3-030-88942-5_4). @article{info:hdl:2013/335292b,Customer churn is a major concern for large companies (notably telcos), even in a big data world. Customer retention campaigns are routinely used to prevent churn, but targeting the right customers on the basis of their historical profile is a difficult task. Companies usually have recourse to two data-driven approaches: churn prediction and uplift modeling. In churn prediction, customers are selected on the basis of their propensity to churn in a near future. In uplift modeling, only customers reacting positively to the campaign are considered. Though uplift is better suited to maximize the efficiency of the retention campaign because of its causal aspect, it suffers from several estimation issues. To improve the uplift accuracy, this paper proposes to leverage historical data about the reachability of customers during a campaign. We suggest several strategies to incorporate reach information in uplift models, and we show that most of them outperform the classical churn and uplift models. This is a promising perspective for churn prevention in the telecommunication sector, where uplift modeling has failed so far to provide a significant advantage over non-causal approaches. |
Paldino, Gian Marco; Stefani, Jacopo De; Caro, Fabrizio De; Bontempi, Gianluca Does AutoML Outperform Naive Forecasting? † Journal Article In: Engineering Proceedings, vol. 5, no. 1, 2021, (DOI: 10.3390/engproc2021005036). @article{info:hdl:2013/361585,The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or machine learning (ML) background and expertise, which is not always available to end-users. This gap fosters a growing demand for frameworks automating the ML pipeline and ensuring broader access to the general public. Automatic machine learning (AutoML) provides solutions to build and validate machine learning pipelines minimizing the user intervention. Most of those pipelines have been validated in static supervised learning settings, while an extensive validation in time series prediction is still missing. This issue is particularly important in the forecasting community, where the relevance of machine learning approaches is still under debate. This paper assesses four existing AutoML frameworks (AutoGluon, H2O, TPOT, Auto-sklearn) on a number of forecasting challenges (univariate and multivariate, single-step and multi-step ahead) by benchmarking them against simple and conventional forecasting strategies (e.g., naive and exponential smoothing). The obtained results highlight that AutoML approaches are not yet mature enough to address generic forecasting tasks once compared with faster yet more basic statistical forecasters. In particular, the tested AutoML configurations, on average, do not significantly outperform a Naive estimator. Those results, yet preliminary, should not be interpreted as a rejection of AutoML solutions in forecasting but as an encouragement to a more rigorous validation of their limits and perspectives. |
2023 |
Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution Journal Article In: Corrosion science, vol. 217, pp. 111104, 2023, (DOI: 10.1016/j.corsci.2023.111104). |
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment Journal Article In: Machine learning, 2023, (DOI: 10.1007/s10994-023-06317-w). |
An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection Journal Article In: IEEE access, vol. 11, pp. 136666-136679, 2023, (DOI: 10.1109/ACCESS.2023.3337635). |
Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models Journal Article In: Test, vol. 32, no. 4, pp. 1177-1179, 2023, (DOI: 10.1007/s11749-023-00896-5). |
Discussion on: “A scale-free approach for false discovery rate control in generalized linear models” by Dai, Lin, Zing, Liu. Journal Article In: Journal of the American Statistical Association, vol. 118, no. 543, pp. 1573-1577, 2023, (Language of publication: fr). |
Scale Transfer in 1849: Heinrich Schwabe to Rudolf Wolf Journal Article In: Solar physics, vol. 298, no. 1, pp. 1-12, 2023, (Language of publication: fr). |
2022 |
RedRibbon: A new rank-rank hypergeometric overlap pipeline to compare gene and transcript expression signatures Journal Article In: BioRxiv, 2022, (DOI: https://doi.org/10.1101/2022.08.31.505818). |
Identification of differentially methylated regions in rare diseases from a single-patient perspective Journal Article In: Clinical Epigenetics, vol. 14, no. 1, 2022, (DOI: 10.1186/s13148-022-01403-7). |
In: Epigenetics, vol. 17, no. 13, pp. 2434-2454, 2022, (DOI: 10.1080/15592294.2022.2135201). |
Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants. Journal Article In: Plant and Cell Physiology, vol. 63, no. 10, pp. 1457-1473, 2022, (DOI: 10.1093/pcp/pcac095). |
GNN-based embedding for clustering scRNA-seq data Journal Article In: Bioinformatics, vol. 38, no. 4, pp. 1037-1044, 2022, (DOI: 10.1093/bioinformatics/btab787). |
A knowledge graph approach for interpretable prediction of pathogenic genetic interactions Miscellaneous 2022, (Conference: European Conference on Computational Biology (ECCB) 2022 (2022-07: Sitges, Spain)). |
A New Approach to Handle Non-Stationarity in Collective Decision-Making Miscellaneous 2022, (Conference: ACM Collective Intelligence conference (CI)(Virtual)). |
Inferring Strategies from Observations in Long Iterated Prisoner’s Dilemma Experiments Miscellaneous 2022, (Conference: Complex Systems Conference 2022(17-21/10/2022: Palma de Mallorca, Spain)). |
Taking the prediction of pathogenic variant-combinations to the next level with VarCoPP2.0 Miscellaneous 2022, (Conference: European Conference on Computational Biology (21: 12-21 September 2022: Sitges, Barcelona)). |
2022, (Conference: Rare Med Symposium(8-12-2022: Gent)). |
Bias Mitigation in Decision-Making with Expert Advice Miscellaneous 2022, (Conference: Benelux AI Conference (BNAIC) and Benelux machine learning conference (Benelearn)(7-9/11/2022: Antwerpen, Belgique)). |
A Novel Approach to Handle Non-stationarity in Collective Decision-Making with Experts Miscellaneous 2022, (Conference: ACM Collective Intelligence Conference 2022(20-21 Octobre 2022: Online)). |
Identification of novel type 1 and type 2 diabetes genes by colocalisation of human islet eQTL and GWAS variants Miscellaneous 2022, (Conference: EASD Annual Meeting of the European Association for the Study of Diabetes(58th: 19 – 23 September 2022: Stockholm, Sweden)). |
The art of compensation : how hybrid teams solve collective risk dilemmas Miscellaneous 2022, (Conference: Adaptive and Learning Agents (ALA) Workshop(9-10/5/2022: Auckland, NZ)). |
Fast deliberation is related to unconditional behaviour in iterated Prisoners’ Dilemma experiments Journal Article In: Scientific Reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-24849-4). |
Scaling up oligogenic diseases research with OLIDA: The Oligogenic Diseases Database Journal Article In: Database, vol. 2022, 2022, (DOI: 10.1093/database/baac023). |
Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma Journal Article In: Scientific reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-11518-9). |
Voluntary safety commitments provide an escape from over-regulation in AI development Journal Article In: Technology in society, vol. 68, 2022, (DOI: 10.1016/j.techsoc.2021.101843). |
A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines Journal Article In: Energies, vol. 15, no. 6, 2022, (DOI: 10.3390/en15062254). |
Information criteria bias correction for group selection Journal Article In: Statistical papers, 2022, (Language of publication: fr). |
Artificial intelligence development races in heterogeneous settings Journal Article In: Scientific reports, vol. 12, no. 1, 2022, (DOI: 10.1038/s41598-022-05729-3). |
Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans. Journal Article In: PloS one, vol. 17, no. 1, pp. e0262417, 2022, (DOI: 10.1371/journal.pone.0262417). |
Wavelets from a Statistical Perspective Book CRC Press, 2022, (Language of publication: fr). |
2021 |
Quality Assessment of Sunspot data using different catalogs Miscellaneous 2021, (Conference: Virtual Conference on Applications of Statistical Methods and Machine Learning in the Space Sciences (17-21 May 2021: Space Science Institute, Boulder, Colorado)). |
TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Journal Article In: Cell reports, vol. 37, no. 2, pp. 109807, 2021, (DOI: 10.1016/j.celrep.2021.109807). |
Time-scale Differences will Influence the Regulation Required in an Idealised AI Race Game Miscellaneous 2021, (Conference: International Joint Conference on Artificial Intelligence (IJCAI)(30: 19-26/8/2021: Montreal, Canada)). |
Voluntary safety commitments provide an escape from over-regulation in AI development Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon,France)). |
DNAModAnnot: A R toolbox for DNA modification filtering and annotation Journal Article In: Bioinformatics, vol. 37, no. 17, pp. 2738-2740, 2021, (DOI: 10.1093/bioinformatics/btab032). |
Molecular adaptations to heat stress in the thermophilic ant genus Cataglyphis Journal Article In: Molecular ecology, 2021, (DOI: 10.1111/mec.16134). |
Contrastive self-supervised clustering of scRNA-seq data. Journal Article In: BMC bioinformatics, vol. 22, no. 1, pp. 280, 2021, (DOI: 10.1186/s12859-021-04210-8). |
Mediating Artificial Intelligence Developments through Negative and Positive Incentives Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon, France)). |
Heterogeneous Interactions in Artificial Intelligence Development Races Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon. France)). |
Modeling behavioral experiments on uncertainty and cooperation with population-based reinforcement learning Miscellaneous 2021, (Conference: Artificial Life Conference(19-23/7/2021: Prague, Czech Republic)). |
Optimization algorithm for omic data subspace clustering Proceedings Article In: CSBio2021: The 12th International Conference on Computational Systems-Biology and Bioinformatics, pp. Pages 69–89, 2021, (Conference: CSBio2021). |
2021, (DOI: 10.1007/978-3-030-65154-1). |
Statistical foundations of machine learning Book 2021, (Language of publication: en). |
The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article In: Ethics and information technology, 2021, (DOI: 10.1007/s10676-020-09567-7). |
Voluntary safety commitments provide an escape from over-regulation in AI development Miscellaneous 2021, (Conference: International Conference on Complex Systems(25-29/10/2021: Lyon,France)). |
DOME: recommendations for supervised machine learning validation in biology Journal Article In: Nature methods, 2021, (DOI: 10.1038/s41592-021-01205-4). |
NR5A1 c.991-1G > C splice-site variant causes familial 46,XY partial gonadal dysgenesis with incomplete penetrance Journal Article In: Clinical endocrinology, 2021, (DOI: 10.1111/cen.14381). |
Multistage feedback-driven compartmental dynamics of hematopoiesis Journal Article In: iScience, vol. 24, no. 4, 2021, (DOI: 10.1016/j.isci.2021.102326). |
Transfer Learning Strategies for Credit Card Fraud Detection Journal Article In: IEEE access, vol. 9, pp. 114754-114766, 2021, (DOI: 10.1109/ACCESS.2021.3104472). |
Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention Journal Article In: Lecture notes in computer science, vol. 12986 LNAI, pp. 44-54, 2021, (DOI: 10.1007/978-3-030-88942-5_4). |
Does AutoML Outperform Naive Forecasting? † Journal Article In: Engineering Proceedings, vol. 5, no. 1, 2021, (DOI: 10.3390/engproc2021005036). |