Publications depuis 2020

Anaïs Baudot — mis à jour juillet 2026

Anaïs Baudot  |  Membre de l'équipe

Articles scientifiques (peer-reviewed) (n = 35)

2026

Fanchon E, Loire B, Trani JP, Magdinier F, Baudot A. Pylluminator: fast and scalable analysis of DNA methylation data in Python. Bioinformatics Advances. 2026;6(1):vbag146. doi: 10.1093/bioadv/vbag146

2025

Hirst DP, Térézol M, Cantini L, Villoutreix P, Vignes M, Baudot A. MOTL: enhancing multi-omics matrix factorization with transfer learning. Genome Biology. 2025;26:224. doi: 10.1186/s13059-025-03675-7

Ben Boina N, Mossé B, Baudot A, Remy É. Refining Boolean models with the partial most permissive scheme. Bioinformatics. 2025;41(4):btaf123. doi: 10.1093/bioinformatics/btaf123

Lambert J, Leutenegger AL, Baudot A, Jannot AS. Improving patient clustering by incorporating structured variable label relationships in similarity measures. BMC Medical Research Methodology. 2025;25:72. doi: 10.1186/s12874-025-02459-8

Loo RTJ, Nasta F, Macchi M, Baudot A, Burstein F, Bove R, Greve M, Fröhlich H, Khalid S, Küderle A, Moore SL, Storms V, Torous J, Glaab E. Recommendations for Successful Development and Implementation of Digital Health Technology Tools. J Med Internet Res. 2025. doi: 10.2196/56747

Ogloblinsky MC, Conrad DF, Baudot A, Tournier-Lasserve E, FrEx Consortium, Génin E, Marenne G. Benchmark of computational methods to detect digenism in sequencing data. Eur J Hum Genet. 2025. doi: 10.1038/s41431-025-01834-9

Ozisik O, Kara NS, Abbassi-Daloii T, Térézol M, Kuijper EC, Queralt-Rosinach N, Jacobsen A, Sezerman OU, Roos M, Evelo CT, Baudot A, Ehrhart F, Mina E. A collaborative network analysis for the interpretation of transcriptomics data in Huntington's disease. Scientific Reports. 2025. doi: 10.1038/s41598-025-85580-4

Toury L, Frankel D, Nael S, Abaji M, Le Goff L, Basset M, Airault C, Vernay B, Novoa-del-Toro EM, Bartoli C, Baudot A, Magdinier F, Kaspi E, Roll P. miR-140-5p Overexpression Contributes to Oxidative Stress and Mitochondrial Dysfunction in Hutchinson-Gilford Progeria Syndrome Fibroblasts Through NRF2 Pathway. Aging Cell. 2025. doi: 10.1111/acel.70276

De Bono C, Xu Y, Kausar S, Herbane M, Humbert C, Rafatov S, Missirian C, Moreno M, Shi W, Gitton Y, Lombardini A, Vanzetta I, Mazaud-Guittot S, Chédotal A, Baudot A, Zaffran S, Etchevers HC. Multi-modal refinement of the human heart atlas during the first gestational trimester. Development. 2025;152(5). doi: 10.1242/dev.204555

Kara NS, Ozisik O, Baudot A, Slachtova L. Investigating the Potential Roles of Environmental Exposures on the Pathology of Amyotrophic Lateral Sclerosis by Overlap Analysis. Neurotox Res. 2025;43(6):51. doi: 10.1007/s12640-025-00774-y

Wijnbergen D, Johari M, Ozisik O, 't Hoen PAC, Ehrhart F, Baudot A, Evelo CT, Udd B, Roos M, Mina E. Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression. Orphanet J Rare Dis. 2025;20(1):27. doi: 10.1186/s13023-024-03526-x

2024

Baptista A, Brière G, Baudot A. Random Walk with Restart on multilayer networks: from node prioritisation to supervised link prediction and beyond. BMC Bioinformatics. 2024;25:70.

Térézol M, Baudot A, Ozisik O. ODAMNet: a Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches. SoftwareX. 2024;26:101701. doi: 10.1016/j.softx.2024.101701

Beust C, Valdeolivas A, Baptista A, Brière G, Lévy N, Ozisik O, Baudot A. The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration. Advanced Biology. 2024. doi: 10.1002/adbi.202400134

Ozisik O, Gorokhova S, Cerino M, Bartoli M, Baudot A. System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution. Scientific Reports. 2024. doi: 10.1038/s41598-024-60761-9

Bayjanov JR, Doornbos C, Ozisik O, Shin W, Queralt-Rosinach N, Wijnbergen D, Saulnier-Blache JS, Schanstra JP, Buffin-Meyer B, Klein J, Fernández JM, Kaliyaperumal R, Baudot A, 't Hoen PAC, Ehrhart F. Integrative analysis of multi-omics data reveals importance of collagen and the PI3K-AKT signalling pathway in CAKUT. Scientific Reports. 2024. doi: 10.1038/s41598-024-71721-8

Zitnik M, Li MM, Wells A, Glass K, Gysi DM, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, et al.. Current and future directions in network biology. Bioinformatics Advances. 2024;4(1):vbae099. doi: 10.1093/bioadv/vbae099

Argiro L, Chevalier C, Choquet C, Nandkishore N, Ghata A, Baudot A, Zaffran S, Lescroart F. Gastruloids are competent to specify both cardiac and skeletal muscle lineages. Nat Commun. 2024;15:10172. doi: 10.1038/s41467-024-54466-w

Ogloblinsky MC, Bocher O, Aloui C, Leutenegger AL, Ozisik O, Baudot A, Tournier-Lasserve E, Castillo-Madeen H, Lewinsohn D, Conrad DF, Génin E, Marenne G. PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis. Genet Epidemiol. 2024;49(1):e22593. doi: 10.1002/gepi.22593

van Karnebeek CDM, O'Donnell-Luria A, Baynam G, Baudot A, et al.. Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases. Orphanet J Rare Dis. 2024;19(1):357. doi: 10.1186/s13023-024-03361-0

2023

Lescouzères L, Hassen-Khodja C, Baudot A, Bordignon B, Bomont P. A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN. EMBO Mol Med. 2023;e16267.

Lambert J, Leutenegger AL, Jannot AS, Baudot A. Tracking clusters of patients over time enables extracting information from medico-administrative databases. J Biomed Inform. 2023;139:104309.

Baptista A, Sánchez-García RJ, Baudot A, Bianconi G. Zoo guide to network embedding. J Phys Complex. 2023;4(4). doi: 10.1088/2632-072X/ad0e23

Laberthonnière C, Delourme M, Chevalier R, Dion C, Ganne B, Hirst D, Caron L, Perrin P, Adélaïde J, Chaffanet M, Xue S, Nguyen K, Reversade B, Déjardin J, Baudot A, Robin JD, Magdinier F. In skeletal muscle and neural crest cells, SMCHD1 regulates biological pathways relevant for Bosma syndrome and facioscapulohumeral dystrophy phenotype. Nucleic Acids Res. 2023;51(14):7269-7287. doi: 10.1093/nar/gkad523

2022

Baptista A, Gonzalez A, Baudot A. Universal multilayer network exploration by random walk with restart. Communications Physics. 2022;5:170.

Ozisik O, Térézol M, Baudot A. orsum: a Python package for filtering and comparing enrichment analyses using a simple principle. BMC Bioinformatics. 2022;23(1):293.

Ozisik O, Ehrhart F, Evelo CT, Mantovani A, Baudot A. Overlap of vitamin A and vitamin D target genes with CAKUT-related processes. F1000Res. 2022;10:395.

Frankel D, Delecourt V, Novoa-del-Toro EM, Robin JD, Airault C, Bartoli C, Baudot A, et al.. miR-376a-3p and miR-376b-3p overexpression in Hutchinson-Gilford progeria fibroblasts inhibits cell proliferation and induces premature senescence. iScience. 2022;103757.

Lambert J, Leutenegger AL, Jannot AS, Baudot A. Tracking Temporal Clusters from Patient Networks. Stud Health Technol Inform. 2022;294:155-156. doi: 10.3233/SHTI220427

2021

Laberthonnière C, Novoa-del-Toro EM, Chevalier R, Broucqsault N, Rao VV, Trani JP, Baudot A, et al.. AKT Signaling Modifies the Balance between Cell Proliferation and Migration in Neural Crest Cells from Patients Affected with Bosma Arhinia and Microphthalmia Syndrome. Biomedicines. 2021;9(7):751.

Laberthonnière C, Novoa-del-Toro E, Delourme M, Chevalier R, Broucqsault N, Mazaleyrat K, Baudot A, et al.. Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells-derived innervated muscle fibres. J Cachexia Sarcopenia Muscle. 2021.

Novoa-del-Toro EM, Mezura-Montes E, Vignes M, Térézol M, Magdinier F, Tichit L, Baudot A. A multi-objective genetic algorithm to find active modules in multiplex biological networks. PLoS Comput Biol. 2021;17(8):e1009263.

Pio-Lopez L, Valdeolivas A, Tichit L, Remy É, Baudot A. MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach. Scientific Reports. 2021.

Cantini L, Zakeri P, Hernandez C, Naldi A, Thieffry D, Remy E, Baudot A. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications. 2021;12.

2020

Sánchez-Valle J, Tejero H, Fernández JM, Juan D, Capella-Gutiérrez S, Al-Shahrour F, Tabarés-Seisdedos R, Baudot A*, Pancaldi V*, Valencia A*. Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships. Nat Commun. 2020;11(1):2854.

Prépublications (preprints) (n = 9)

2026

Homberg N, Lamothe L, Amblard E, Barbot H, Térézol M, et al. (HADACA3 Consortium, incl. Baudot A), Blum Y, Richard M. How to organise a scientific competition to benchmark methods and algorithms in computational biology?. HAL preprint. 2026.

Berardelli S, Brière G, Loire B, Paoli FD, Gazzo A, Limongelli I, et al., Baudot A. PhenoXtract: combining Large Language Model and Knowledge Graph embedding to extract phenotypes from clinical descriptions. bioRxiv. 2026. doi: 10.64898/2026.06.22.733382

Barbot H, Amblard E, Homberg N, Lamothe L, Térézol M, et al. (HADACA3 Consortium, incl. Baudot A), Richard M. On the Promises and Limits of Multi-omics Integration for Deconvolution: The HADACA3 Benchmark. arXiv. 2026. arXiv:2606.05980.

Baratta P, Villoutreix P, Baudot A. Differential Analysis of Gene Spatial Organisation with Minkowski Functionals and Tensors. bioRxiv. 2026. doi: 10.64898/2026.05.12.724373

Torrejón E, Sleegers J, Matthiesen R, Macedo MP, Baudot A*, Machado de Oliveira R*. EV-Net: A computational framework to model extracellular vesicles-mediated communication. bioRxiv. 2026. doi: 10.64898/2026.04.02.716053

2025

Brière G, Beust C, Térézol M, Baudot A. Using Networks and Prior Knowledge to Uncover novel Rare Disease Phenotypes. medRxiv. 2025. doi: 10.1101/2025.04.02.25325098

Lepe-Soltero D, Artières T, Baudot A, Villoutreix P. MODIS: Multi-Omics Data Integration for Small and unpaired datasets. arXiv. 2025. arXiv:2503.18856.

Brière G, Stosskopf T, Loire B, Baudot A. Benchmarking Data Leakage on Link Prediction in Biomedical Knowledge Graph Embeddings. bioRxiv. 2025. doi: 10.1101/2025.01.23.634511

2023

Kausar S, Asif M, Baudot A. scRNAseq_KNIME workflow: a customizable, locally executable, interactive and automated KNIME workflow for single-cell RNA-seq. bioRxiv. 2023. doi: 10.1101/2023.01.14.524084

Hirst, DP.  et al. 2025

MOTL: enhancing multi-omics matrix factorization with transfer learning

Joint matrix factorization is popular for extracting lower dimensional representations of multi-omics data but loses effectiveness with limited samples. Addressing this limitation, we introduce MOTL...
Genome Biology - issue: 1 - volume: 26 - pages: 224.

Ben Boina, N.  et al. 2025

Refining Boolean models with the partial most permissive scheme

Motivation: In systems biology, modeling strategies aim to decode how molecular components interact to generate dynamical behavior. Boolean modeling is more and more used, but the description of the...
- issue: 4 - volume: 41 - pages: btaf123.

Lambert, J.  et al. 2025

Improving patient clustering by incorporating structured variable label relationships in similarity measures

Background Patient stratification is the cornerstone of numerous health investigations, serving to enhance the estimation of treatment efficacy and facilitating patient matching. To stratify patients,...
BMC Med Res Methodol - issue: 1 - volume: 25 - pages: 72.

Ozisik, O.  et al. 2025

A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease


Sci Rep - issue: 1 - volume: 15 - pages: 1412.

Baptista, A.  et al. 2024

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

Background:  Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous...
BMC Bioinformatics - issue: - volume: - pages: .

Beust, C.  et al. 2024

The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration


- issue: - volume: - pages: .

Argiro, L.  et al. 2024

Gastruloids are competent to specify both cardiac and skeletal muscle lineages

Abstract Cardiopharyngeal mesoderm contributes to the formation of the heart and head muscles. However, the mechanisms governing cardiopharyngeal mesoderm specification remain unclear....
Nat Commun - issue: 1 - volume: 15 - pages: 10172.

Van Karnebeek, CDM.  et al. 2024

Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium...
Orphanet J Rare Dis - issue: 1 - volume: 19 - pages: 357.

Bayjanov, JR.  et al. 2024

Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT

Abstract Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults....
Sci Rep - issue: 1 - volume: 14 - pages: 20731.

Ozisik, O.  et al. 2024

System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution

Abstract Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal...
Sci Rep - issue: 1 - volume: 14 - pages: 11225.

Térézol, M.  et al. 2024

ODAMNet: A Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches

Environmental factors are external conditions that can affect the health of living organisms. For a number of rare genetic diseases, an interplay between genetic and environmental factors is known or...
SoftwareX - issue: - volume: 26 - pages: 101701.

Zitnik, M.  et al. 2024

Current and future directions in network biology

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across...
- issue: 1 - volume: 4 - pages: vbae099.

Lambert, J.  et al. 2023

Tracking clusters of patients over time enables extracting information from medico-administrative databases

Objective: We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. Material and Methods: We first...
Journal of Biomedical Informatics - issue: - volume: 139 - pages: 104309.

Ozisik, O.  et al. 2022

orsum: a Python package for filtering and comparing enrichment analyses using a simple principle

Background:  Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the...
BMC Bioinformatics - issue: 1 - volume: 23 - pages: 293.

Baptista, A.  et al. 2022

Universal multilayer network exploration by random walk with restart

The amount and variety of data have been increasing drastically for several years. These data are often represented as networks and explored with approaches arising from network theory. Recent years...
Communications Physics - issue: 1 - volume: 5 - pages: 170.

Cantini, L.  et al. 2021

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated.
Nature Comm - issue: 1 - volume: 12 - pages: .

Pio-Lopez, L.  et al. 2021

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Abstract Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks...
Sci Rep - issue: 1 - volume: 11 - pages: 8794.

Novoa-del-Toro, EM.  et al. 2021

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular...
PLoS Comput Biol - issue: 8 - volume: 17 - pages: e1009263.

Sánchez-Valle, J.  et al. 2020

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships

Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence...
Nature Comm - issue: 1 - volume: 11 - pages: 2854.

Katsogiannou, M.  et al. 2019

Integrative proteomic and phosphoproteomic profiling of prostate cell lines

Background Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells,...
PLoS ONE - issue: 11 - volume: 14 - pages: 25.

The DREAM Module Identification Challenge Consortium, .  et al. 2019

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms...
Nature Methods - issue: 9 - volume: 16 - pages: 843-852.

Valdeolivas, A.  et al. 2018

Random Walk with Restart on Multiplex and Heterogeneous Biological Networks

Motivation: Recentyears have witnessed anexponentialgrowthin thenumberof identified interactions between biological molecules. These interactions are usually represented as large and complex networks,...
Bioinformatics - issue: - volume: - pages: .

Didier, G.  et al. 2018

Identifying communities from multiplex biological networks by randomized optimization of modularity

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate...
F1000Research - issue: - volume: 7 - pages: 1042.

Sánchez-Valle, J.  et al. 2017

A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the...
Sci Rep - issue: 1 - volume: 7 - pages: 4474.

Tabarés-Seisdedos, R.  et al. 2016

Editorial: Direct and Inverse Comorbidities Between Complex Disorders


Front Physiol - issue: - volume: 7 - pages: 117.

Didier, G.  et al. 2015

Identifying communities from multiplex biological networks

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is...
- issue: - volume: 3 - pages: e1525.

Flobak, A.  et al. 2015

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug...
PLoS Comput. Biol. - issue: 8 - volume: 11 - pages: e1004426.

Tripathi, S.  et al. 2015

The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms

BACKGROUND: The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator...
BMC Syst Biol - issue: - volume: 9 - pages: 40.

Ibáñez, K.  et al. 2014

Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses

There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than expected probability of developing some types of Cancer. We tested here the...
PLoS Genet. - issue: 2 - volume: 10 - pages: e1004173.