Publications

2024

G. Guichaoua, P. Pinel, B. Hoffmann, C.-A. Azencott, and V. Stoven. Advancing drug-target interactions prediction: leveraging a large-scale dataset with a rapid and robust chemogenomic algorithm. bioRxiv, 2024. doi: 10.1101/2024.02.22.581599 [link]
M. Michel, M. Heidary, A. Mechri, K. Da Silva, M. Gorse, V. Dixon, K. von Grafenstein, C. Hego, A. Rampanou, C. Lamy, M. Kamal, C. Le Tourneau, M. Séné, I. Bièche, C. Reyes, D. Gentien, M.-H. Stern, O. Lantz, L. Cabel, J.-Y. Pierga, F.-C. Bidard, C.-A. Azencott, and C. Proudhon. Non-invasive multi-cancer diagnosis using DNA hypomethylation of LINE-1 retrotransposons. medRxiv, 2024. doi: 10.1101/2024.01.20.23288905 [link]
M. Sheinman, P. F. Arndt, F. Massip, Modeling the mosaic structure of bacterial genomes to infer their evolutionary history. PNAS, March 2024. doi: 10.1073/pnas.2313367121 [link]
de Biase, M. S., Massip, F., Wei, T. T., Giorgi, F. M., Stark, R., Stone, A., Gladwell A., O'Reilly M., de Santiago I., Meyer K., Markowetz F., Ponder B. A. J., Rintoul R. C., Schwarz, R. F. Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk, Genome Medicine, 2024. doi: 10.1186/s13073-024-01317-4 . [link]

2023

T. Lazard, M. Lerousseau, S. Gardrat, A. Vincent-Salomon, M.-H. Stern, M. Rodrigues, E. Decencière, and T. Walter. Democratizing computational pathology: Optimized Whole Slide Image representations for The Cancer Genome Atlas, BioRXiv, 2023. doi: 10.1101/2023.12.04.569894 [link].
T. Defard, H. Laporte, M. Ayan, S. Juliette, S. Curras-Alonso, C. Weber, F. Massip, J.-A. Londoño-Vallejo, C. Fouillade, F. Mueller, and T. Walter. A point cloud segmentation framework for image-based spatial transcriptomics, BioRXiv, 2023. doi: 10.1101/2023.12.01.569528 [link].
T. Lazard, G. Bataillon, T. Walter, and A. Vincent Salomon. Cancer du sein - utilisation de l’intelligence artificielle pour prédire le statut tumoral relatif à la recombinaison homologue, Med Sci (Paris), vol. 39, no. 12, pp. 926–928, 2023. doi: 10.1051/medsci/2023169 [link].
A. Behdenna, M. Colange, J. Haziza, A. Gema, G. Appé, C.-A. Azencott and A. Nordor. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics 24, 459, 2023. doi: 10.1186/s12859-023-05578-5 [link]
M. Najm, M. Cornet, L. Albergante, A. Zinovyev, I. Sermet-Gaudelus, V. Stoven, L. Calzone and L. Martignetti. Representation and quantification Of Module Activity from omics data with rROMA. NPJ Systems Biology and Applications, (in press).
S. Rorigues-Ferreira, M. Morin, G. Guichaoua, H. Moindjie, M. M. Haykal, O. Collier, V. Stoven and C. Nahmias. A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in Breast Cancer. Cancers (Basel), 202, 15(19):4870. doi: 10.3390/cancers15194870 [link].
M. Lubrano, Y. Bellahsen-Harrar, S. Berlemont, T. Walter, and C. Badoual. Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract, Histopathology, p. his.15067, Oct. 2023, doi: 10.1111/his.15067
D.B. Page, G. Broeckx, C. A. Jahangir, S. Verbandt, et al., Spatial analyses of immune cell infiltration in cancer: Current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer, The Journal of Pathology, vol. 260, no. 5, pp. 514–532, Aug. 2023, doi: [link].
J. Thagaard, G. Broeckx, D. B. Page, C. A. Jahangir, et al.,Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer, The Journal of Pathology, vol. 260, no. 5, pp. 498–513, Aug. 2023, doi: 10.1002/path.6155 [link].
P. F. Arndt, F. Massip, M. Sheinman, An analytical derivation of the distribution of distances between heterozygous sites in diploid species to efficiently infer demographic history, in bioRXiv, 2023. doi: 10.1101/2023.09.20.558510 [link]
A. Faiz, R. M. Mahbub, E. S. Boedijono, M. I. Tomassen, W. Kooistra, W. Timens, M. Nawijn, P. M. Hansbro, M. D. Johansen, S. D. Pouwels, I. H. Heijink, F. Massip, M. S. de Biase, R. F. Schwarz, I. M. Adcock, K. F. Chung, A. van der Does, P. S. Hiemstra, H. Goulaouic, H. Xing, R. Abdulai, E. de Rinaldis, D. Cunoosamy, S. Harel, D. Lederer, M. C. Nivens, P. A. Wark, H. A. M. Kerstjens, M. N. Hylkema, C. A. Brandsma, M. van den Berge, Cambridge Lung Cancer Early Detection Programme. IL-33 Expression Is Lower in Current Smokers at Both Transcriptomic and Protein Level, in. Am J Respir Crit Care Med. 2023 Sep 14. doi: 10.1164/rccm.202210-1881OC [link]. Epub ahead of print. PMID: 37708400.
J. Poulet-Benedetti, C. Tonnerre-Doncarli, A.-L. Valton, M. Laurent, M. Gérard, N. Barinova, N. Parisis, F. Massip, F. Picard, M.-N. Prioleau. Dimeric G-quadruplex motifs-induced NFRs determine strong replication origins in vertebrates, in Nature communications, Aug 2023. doi: 10.1038/s41467-023-40441-4 [link]
T. Lazard, M. Lerousseau, E. Decencière, and T. Walter, Giga-SSL: Self-supervised learning for gigapixel images, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops, Jun. 2023, pp. 4304–4313.
D. Zyss, S. A. Ribeiro, M. J. C. Ludlam, T. Walter, and A. Fehri. Cell Segmentation in Images Without Structural Fluorescent Labels, Biol. Imaging, pp. 1–18, Jul. 2023, doi: 10.1017/S2633903X23000168 [link]
M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter. Simple and Eﬃcient Conﬁdence Score for Grading Whole Slide Images, in Proceedings of Machine Learning Research, Jul. 2023, vol. 58, pp. 1–19.
Y. Bellahsen-Harrar, M. Lubrano, C. Lépine, A. Beaufrère, C. Bocciarelli, A. Brunet, E. Decroix, F. N. El-Sissy, B. Fabiani, A. Morini, C. Tilmant, T. Walter, and C. Badoual. AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level, Pathology, preprint, Jul. 2023. doi: 10.1101/2023.07.23.23292962 [link]
C. Poulet, A. Debit, C. Josse, G. Jerusalem, C.-A. Azencott, V. Bours and K. Van Steen. Assessing random forest self-reproducibility for optimal short biomarker signature discovery, BioRxiv 2023. [link]
S. Curras-Alonso, J. Soulier, T. Defard, C. Weber, S. Heinrich, H. Laporte, S. Leboucher, S. Lameiras, M. Dutreix, V. Favaudon, F. Massip, T. Walter, F. Mueller, J.-A. Londoño-Vallejo, and C. Fouillade. An interactive murine single-cell atlas of the lung responses to radiation injury, Nat Commun, vol. 14, no. 1, p. 2445, Apr. 2023, doi: 10.1038/s41467-023-38134-z [link]
M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter, A simple and efficient confidence score for grading whole slide images. arXiv, Mar. 08, 2023, [link].
C. Le Priol, C.-A. Azencott, and X. Gidrol. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression, PLoS Computational Biology 19(3): e1010342 2023 [link]
T. Bonte, M. Philbert, E. Coleno, E. Bertrand, A. Imbert, and T. Walter, Learning with minimal effort: Leveraging in silico labeling for cell and nucleus segmentation. arXiv, Jan. 10, 2023, [link].
A. Imbert, F. Mueller, and T. Walter, PointFISH – learning point cloud representations for RNA localization patterns. arXiv, Feb. 21, 2023, [link].
P. Pinel, G. Guichaoua, M. Najm, S. Labouille, N. Drizard, Y. Gaston-Mathé, B. Hoffmann and V. Stoven. Exploring isofunctional molecules: design of a benchmark and evaluation of prediction performance. Molecular Informatics, 2023. doi: 10.1002/minf.202200216. [link]
H. Climente-González, C.-A. Azencott, and M. Yamada. A network-guided protocol to discover susceptibility genes in genome-wide association studies using stability selection, STAR Protoc 2023. [link]

2022

T. Lazard, G. Bataillon, P. Naylor, T. Popova, F.-C. Bidard, D. Stoppa-Lyonnet, M.-H. Stern, E. Decencière, T. Walter, and A. Vincent-Salomon; Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. Cell Reports Medicine, Dec. 2022, doi: 10.1016/j.xcrm.2022.100872 [link]
M. Cornet, M. Najm, L. Albergante, A. Zinovyev, I. Sermet-Gaudelus, V. Stoven, L. Calzone, L. Martignetti, Representation and quantification Of Module Activity from omics data with rROMA, doi: 10.1101/2022.10.24.513448, bioRxiv, oct 2022. [preprint]
D. Drummond, J. Dana, L. Berteloot, E. K. Schneider-Futschik, F. Chedevergne, C. Bailly-Botuha, T. Nguyen-Khoa, M. Cornet, M. Le Bourgeois, D. Debray, M. Girard, I. Sermet-Gaudelus. Lumacaftor-ivacaftor effects on cystic fibrosis-related liver involvement in adolescents with homozygous F508 del-CFTR. J Cyst Fibros, 2022 21(2):212-219. doi: 10.1016/j.jcf.2021.07.018. [link]
E. Dumas, A.-S. Hamy, S. Houzard, E. Hernandez, A. Toussaint, J. Guerin, L. Chanas, V. de Castelbajac, M. Saint-Ghislain, B. Grandal, E. Daoud, F. Reyal, and C.-A. Azencott, EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data, Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022 [preprint].
A. Imbert, F. Mueller, and T. Walter, PointFISH: Learning Point Cloud Representations for RNA Localization Patterns, in Bioimage Conference (BIC) at the on Computer Vision (ECCV), Oct. 2022, p. 17.
T. Bonte, M. Philbert, E. Coleno, A. Imbert, and T. Walter, Learning with minimal effort: Leveraging in silico labeling for cell and nucleus segmentation, in Bioimage Computing (BIC) at the European Conference on Computer Vision (ECCV), Oct. 2022, p. 14.
C. Vesteghem, W. M. Szejniuk, R. F. Brøndum, U. G. Falkmer, C.-A. Azencott, and M. Bøgsted. Dynamic risk prediction of 30-day mortality of patients with advanced lung cancer: Comparing 5 machine learning approaches, JCO Clinical Cancer Informatics no. 6 (2022) e2200054 [link]
M. Lubrano, T. Lazard, G. Balezo, Y. Bellahsen-Harrar, C. Badoual, S. Berlemont, and T. Walter, Automatic grading of cervical biopsies by combining full and self-supervision, in Workshop on AI-enabled medical image analysis (AIMIA) at the European Conference on Computer Vision (ECCV), Oct. 2022, p. 14.
A. Safieddine, E. Coleno, F. Lionneton, A.-M. Traboulsi, S. Salloum, C.-H. Lecellier, T. Gostan, V. Georget, C. Hassen-Khodja, A. Imbert, F. Mueller, T. Walter, M. Peter, and E. Bertrand, HT-smFISH: A cost-effective and flexible workflow for high-throughput single-molecule RNA imaging, Nature Protocols, Oct. 2022, doi: 10.1038/s41596-022-00750-2.
C. Le Priol, C.-A. Azencott, and X. Gidrol. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression, BioRxiv 2022. [link]
P. Naylor, T. Lazard, G. Bataillon, M. Laé, A. Vincent-Salomon, A.-S. Hamy, F. Reyal, and T. Walter. Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images. Frontiers in Signal Processing, vol. 2, p. 851809, Jun. 2022, doi: 10.3389/frsip.2022.851809.
M. Cornet, G. Robin, F. Ciciriello, T. Bihouee, C. Marguet, V. Roy, M. Lebourgeois, F. Chedevergne, A.-S. Bonnel, M. Kelly, P. Reix, V. Lucidi, V. Stoven, and I. Sermet-Gaudelus. Profiling the response to lumacaftor-ivacaftor in children with cystic between fibrosis and new insight from a French-Italian real-life cohort. Pediatr Pulmonol. 2022 Aug 22 [link].
E. Daoud, A.-S. Hamy-Petit, E. Dumas, L. Delrieu, B. Grandal Rejo, C. Le Bihan-Benjamin, S. Houzard, P.-J. Bousquet, J. Hotton, A.-M. Savoye, C. Jouannaud, C.-A. Azencott, M. Lelarge, and F. Reyal. Disparities in accessibility to oncology care centers in France. BioRxiv 2022, [preprint].
B. Chevalier, N. Baatallah, M. Najm, S. Castanier, V. Jung, I. Pranke, A. Golec, V. Stoven, S. Marullo, F. Antigny, I. C. Guerrera, I. Sermet-Gaudelus, A. Edelman, and A. Hinzpeter. Differential CFTR-Interactome Proximity Labeling Procedures Identify Enrichment in Multiple SLC Transporters. Int J Mol Sci. 2022, 23(16):8937 [link].
C.-A. Azencott. Introduction au Machine Learning (2ème édition). Dunod InfoSup, 2022. [link]
L. Slim, H. de Foucauld, C. Chatelain, and C.-A. Azencott. A systematic analysis of gene-gene interaction in multiple sclerosis, BMC Medical Genomics 15:100 (2022) [link].
E. Dumas, L. Laot, F. Coussy, B. Grandal Rejo, E. Daoud, E. Laas, A. Kassara, A. Majdling, R. Kabirian, F. Jochum, P. Gougis, S. Michel, S. Houzard, C. Le Bihan-Benjamin, P.-J. Bousquet, J. Hotton, C.-A. Azencott, F. Reyal, and A.-S. Hamy, The French Early Breast Cancer Cohort (FRESH): a resource for breast cancer research and evaluations of oncology practices based on the French National Healthcare System Database (SNDS), Cancers 2022 14(11), 2671. [preprint] [link]
D. Duroux, H. Climente-González, C.-A. Azencott, and K. Van Steen. Interpretable network-guided epistasis detection, GigaScience 11:giab093, 2022. [link].
M. Lubrano di Scandalea, T. Lazard, G. Balezo, Y. Bellahsen-Harrar, C. Badoual, S. Berlemont, and T. Walter, Automatic grading of cervical biopsies by combining full and self-supervision, BioRxiv, Jan. 2022, [link]
A. Nouira, C.-A. Azencott. Multitask group Lasso for Genome Wide Association Studies in diverse populations, Pacific Symposium on Biocomputing 27:163-174, 2022. [preprint] [link]
L. Slim, C. Chatelain, and C.-A. Azencott. Nonlinear post-selection inference for genome-wide association studies, Pacific Symposium on Biocomputing 27:349-360, 2022. [preprint] [link]
A. Jouinot, J. Lippert, M. Sibony, L. Jeanpierre, D. De Murat, R. Armignacco, A. Septier, K. Perlemoine, F. Letourneur, B. Izac, B. Ragazzon, K. Leroy, E. Pasmant, M.O. North, S. Gaujoux, B. Dousset, L. Groussin, R. Libe, B. Terris, M. Fassnacht, C.L. Ronchi, J. Bertherat, G. Assié. Transcriptome in paraffin samples for the diagnosis and prognosis of adrenocortical carcinoma, Eur J Endocrinol 2022 Mar 1:EJE-21-1228. [link]
Hananeh Aliee, Florian Massip, Cancan Qi, Maria Stella de Biase, Jos van Nijnatten, Elin TG Kersten, Nazanin Z Kermani, Basil Khuder, Judith M Vonk, Roel CH Vermeulen, U‐BIOPRED study group, Cambridge Lung Cancer Early Detection Programme, INER‐Ciencias Mexican Lung Program, Margaret Neighbors, Gaik W Tew, Michele A Grimbaldeston, Nick HT ten Hacken, Sile Hu, Yike Guo, Xiaoyu Zhang, Kai Sun, Pieter S Hiemstra, Bruce A Ponder, Mika J Mäkelä, Kristiina Malmström, Robert C Rintoul, Paul A Reyfman, Fabian J Theis, Corry‐Anke Brandsma, Ian M Adcock, Wim Timens, Cheng‐Jian Xu, Maarten van den Berge, Roland F Schwarz, Gerard H Koppelman, MC Nawijn, Alen Faiz. Determinants of expression of SARS‐CoV‐2 entry‐related genes in upper and lower airways, Allergy, 2022 Feb. https://onlinelibrary.wiley.com/doi/full/10.1111/all.15152
A. Vaczlavik, L. Bouys, F. Violon, G. Giannone, A. Jouinot, R. Armignacco, I.P. Cavalcante, A. Berthon, E. Letouzé, P. Vaduva, M. Barat, F. Bonnet, K. Perlemoine, C. Ribes, M. Sibony, M.O. North, S. Espiard, P. Emy, M. Haissaguerre, I. Tauveron, L. Guignat, L. Groussin, B. Dousset, M. Reincke, M.C. Fragoso, C.A. Stratakis, E. Pasmant, R. Libé, G. Assié, B. Ragazzon, J. Bertherat. KDM1A inactivation causes hereditary food-dependent Cushing syndrome, Genet Med 2022 Feb;24(2):374-383. [link]

2021

X. Pichon, K. Moissoglu, E. Coleno, T. Wang, A. Imbert, M. Peter, R. Chouaib, T. Walter, F. Mueller, K. Zibara, E. Bertrand, and S. Mili. The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions, RNA 27(12):1528-1544, 2021. [link]
V. Mallet, C. Oliver, J. Broadbent, W.L. Hamilton, J. Waldispühl. RNAglib: A Python Package for RNA 2.5 D Graphs Bioinformatics application notes, Dec 2021. [link]
C. Oliver, V. Mallet, P. Philippopoulos, W.L. Hamilton, J. Waldispühl. VeRNAl: A Tool for Mining Fuzzy Network Motifs in RNA Bioinformatics , Nov 2021. [link]
V. Mallet, L. Checa Ruano, A. Moine Franel, M. Nilges, K. Druart, G. Bouvier, O. Sperandio. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions Bioinformatics , Nov 2021. [link]
V. Mallet, J.P. Vert. Reverse-Complement Equivariant Networks for DNA Sequences NeurIPS proceedings , Dec 2021. [link]
B. Baussart, C. Villa, A. Jouinot, M.L. Raffin-Sanson, L. Foubert, L. Cazabat, M. Bernier, F. Bonnet, A. Dohan, J. Bertherat, G. Assié, S. Gaillard. Pituitary surgery as alternative to dopamine agonists treatment for microprolactinomas: a cohort study, Eur J Endocrinol 2021 Oct 21;185(6):783-791. [link]
T. Lazard, G. Bataillon, P. Naylor, T. Popova, F.-C. Bidard, D. Stoppa-Lyonnet, M.-H. Stern, E. Decencière, T. Walter, and A. V. Salomon. Deep learning identifies new morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. bioRxiv, Sep. 2021. [link]
J. Abecassis, F. Reyal and J.-P. Vert. Clonesig can jointly infer intra-tumor heterogeneity and mutational signature activity in tumor bulk sequencing data. Nature Communications, 12:5352, 2021. [link]
A. Imbert, W. Ouyang, A. Safieddine, E. Coleno, C. Zimmer, E. Bertrand, T. Walter, and F. Mueller. FISH-quant v2: A scalable and modular analysis tool for smFISH image analysis. bioRxiv, Jul. 2021, [link]
E. Daoud, A.-S. Hamy-Petit, E. Dumas, L. Delrieu, B. Grandal Rejo, C. Le Bihan-Benjamin, S. Houzard, P.-J. Bousquet, J. Hotton, A.-M. Savoye, C. Jouannaud, C.-A. Azencott, M. Lelarge, F. Reyal. Disparities in accessibility to oncology care centers in France, medRxiv, 2021. [link]
Y. Jiao, F. Lesueur, C.-A. Azencott, M. Laurent, N. Mebirouk, L. Laborde, J. Beauvallet, M.-G. Dondon, S. Eon-Marchais, A. Laugé, GEMO Study Collaborators, GENEPSO Study Collaborators, C. Noguès, N. Andrieu, D. Stoppa-Lyonnet, and S. M. Caputo. A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers, BMC Medical Research Methodology, 2021.[link]
M. Najm, C.-A. Azencott, B. Playe, and V. Stoven. Drug Target Identification with Machine Learning: How to Choose Negative Examples, International Journal of Molecular Sciences, 2021. [link]
V. Goepp, J.-C. Thalabard, G. Nuel, and O. Bouaziz. Regularized Bidimensional Estimation of the Hazard Rate, The International Journal of Biostatistics, 2021. [link]
A. Behdenna, J. Haziza, C.-A. Azencott, and A. Nordor. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods, bioRxiv, 2021. [link]
H. Climente-González, C. Lonjou, F. Lesueur, GENESIS Study collaborators, D. Stoppa-Lyonnet, N. Andrieu, and C.-A. Azencott. Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer, PLoS Comput Biol 17(3): e1008819, 2021. [link]
A. Safieddine, E. Coleno, S. Salloum, A. Imbert, A.-M. Traboulsi, O. S. Kwon, F. Lionneton, V. Georget, M.-C. Robert, T. Gostan, C.-H. Lecellier, R. Chouaib, X. Pichon, H. Le Hir, K. Zibara, F. Mueller, T. Walter, M. Peter, and E. Bertrand. A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport, Nature Communications, vol. 12, no. 1, pp. 1352, 2021. [link]
S. Curras-Alonso, J. Soulier, T. Walter, F. Mueller, A. Londoño-Vallejo, and C. Fouillade. Spatial transcriptomics for respiratory research and medicine, Eur Respir J, p. 2004314, 2021. [link]
F. Raimundo, L. Papaxanthos, C. Vallot and J.-P. Vert. Machine learning for single cell genomics data analysis. Current Opinion in Systems Biology, 25:64-71, 2021. [link]
L. Delrieu, L. Bouaoun, D. E. Fatouhi, E. Dumas, A.-D. Bouhnik, H. Noelle, E. Jacquet, A.-S. Hamy, F. Coussy, F. Reyal, P.-E. Heudel, M.-K. Bendiane, B. Fournier, M. Michallet, B. Fervers, G. Fagherazzi, O. Pérol. Patterns of Sequelae in Women with a History of Localized Breast Cancer: Results from the French VICAN Survey. Cancers. 2021; 13(5):1161. [link]
N. Varoquaux, W. S. Noble and J.-P. Vert. Inference of genome 3D architecture by modeling overdispersion of Hi-C data. BioRxiv 429864, 2021. [link]
H. Climente-González and C.-A. Azencott. martini: an R package for genome-wide association studies using SNP networks, bioRxiv, 2021. [link]
V. Tozzo, C.-A. Azencott, S. Fiorini, E. Fava, A. Trucco, and A. Barla. Where do we stand in regularization for life science studies? Journal of Computational Biology, 2021. [link]

2020

J. Boitreaud, V. Mallet, C. Oliver, J. Waldispuhl. OptiMol: Optimization of binding affinities in chemical space for drug discovery JCIM , 2020. [link]
C. Oliver, V. Mallet, R.S. Gendron, V. Reinharz, W.L. Hamilton, N. Moitessier, J. Waldispuhl. Augmented base pairing networks encode RNA-small molecule binding preferences. NAR , 2020. [link]
V. Mallet, M. Nilges, G. Bouvier. Quicksom: Self-Organizing maps on GPUs for clustering of molecular dynamics trajectories Bioinformatics Application Notes, 2020. [link]
L. Slim, C. Chatelain, C.-A. Azencott, and J.-P. Vert. Novel methods for epistasis detection in genome-wide association studies. PLoS ONE, 2020. [link]
S. Gauthier, I. Pranke, V. Jung, L. Martignetti, V.Stoven, T. Nguyen-Khoa, M. Semeraro, A. Hinzpeter, A. Edelman, C. Guerrera, and I. Sermet-Gaudelus. Urinary exosomes of patients with Cystic Fibrosis unravel CFTR related renal disease, International Journal for Molecular Sciences, 10;21(18):6625 2020. [link]
L. Slim, H. de Foucauld, C. Chatelain, and C.-A. Azencott. A systematic analysis of gene-gene interaction in multiple sclerosis, bioRxiv, 2020. [link]
M. Balluet, F. Sizaire, T. Walter, J. Pont, B. Giroux, O. Bouchareb, M. Tramier, and J. Pecreaux. Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy. bioRxiv, 2020. [link]
B. Playe and V. Stoven. Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity, Journal of chemoinformatics, 12, 11, 2020. [link]. R. Chouaib, A. Safieddine, X. Pichon, A. Imbert, O. Sung Kwon, A. Samacoits, A.-M. Traboulsi, M.-C. Robert, N. Tsanov, E. Coleno, I. Poser, C. Zimmer, A. Hyman, H. Le Hir, K. Zibara, M. Peter, F. Mueller, T. Walter, and E. Bertrand. A dual protein-mRNA localization screen reveals compartmentalized translation and widespread co-translational RNA targeting. Developmental Cell 54 (6), 773-791, 2020. [link]
P.-C. Aubin-Frankowski and J.-P. Vert. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics 36(18):4774-4780, 2020. [link]
J. Boyd, Z. Gouveia, F. Perez and T. Walter, Experimentally-generated ground truth for detecting cell types in an image-based immunotherapy screen, IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, pp. 886-890, 2020. [link]
M.-M. Aynaud, O. Mirabeau, N. Gruel, S. Grossetête, V. Boeva, S. Durand, D. Surdez, O. Saulnier, S. Zaïdi, S. Gribkova, U. Kairov, V. Raynal, F. Tirode, P. Grünewald, M. Bohec, S. Baulande, I. Janoueix-Lerosey, J.-P. Vert, E. Barillot, O. Delattre and A. Zinovyev. Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single cell resolution. Cell Reports, 30(6):1767-1779.E6, 2020. [link]
K. B. Cook, B. H. Hristov, K. G. Le Roch, J.-P. Vert and W. S. Noble. Measuring significant changes in chromatin conformation with ACCOST. Nucleic Acids Research, 48(5):2303-2311, 2020. [link]

2019

J. Abecassis, A.-S. Hamy, C. Laurent, B. Sadacca, H. Bonsang-Kitzis, F. Reyal and J.-P. Vert. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data. PLoS ONE, 14(11):e0224143, 2019. [link]
O. Collier, V. Stoven, J.-P. Vert. A single- and multi-task machine learning algorithm for the prediction of cancer driver genes. PLoS Computational Biology 15(9):e1007381, 2019. [link]
J. Boyd, A. Pinheiro, E. Del Nery, F. Reyal, T. Walter. Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen. Bioinformatics. [link]
A. G. Cauer, G. Yardimci, J.-P. Vert, N. Varoquaux and W. S. Noble. Inferring diploid 3D chromatin structures from Hi-C data. In K. T. Huber and D. Gusfield (Eds), Proceedings of the 19th International Workshop on Algorithms in Bioinformatics (WABI 2019), Leibniz International Proceedings in Informatics (LIPIcs) 143:11, 2019. [link]
J. Liu, Y. Huang, R. Singh, J.-P. Vert and W. S. Noble. Jointly embedding multiple single-cell omics measurements. In K. T. Huber and D. Gusfield (Eds), Proceedings of the 19th International Workshop on Algorithms in Bioinformatics (WABI 2019), Leibniz International Proceedings in Informatics (LIPIcs) 143:10, 2019. [link]
H. Climente-González, C.-A. Azencott, S. Kaski, M. Yamada. Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data. Bioinformatics, 2019, 35(14) i427–i435, (ISMB/ECCB Proceedings). [link]
L. Slim, C. Chatelain, C.-A. Azencott, J.-P. Vert. kernelPSI: a post-selection inference framework for nonlinear variable selection, Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML), 2019 97:5857—5865. [link]
R. Menegaux and J.-P. Vert. Continuous embeddings of DNA sequencing reads, and application to metagenomics. Journal of Computational Biology, 26(6):509-518, 2019.[link] M. Durand, T. Walter, T. Pirnay, T. Naessens, P. Gueguen, C. Goudot, S. Lameiras, Q. Chang, N. Talaei, O. Ornatsky, T. Vassilevskaia, S. Baulande, S. Amigorena, and E. Segura. Human lymphoid organ cDC2 and macrophages play complementary roles in T follicular helper responses. Journal of experimental medicine, Epub: May 2019. [link]
P. Naylor, J. Boyd, M. Lae, F. Reyal, T. Walter. Predicting Residual Cancer Burden in a Triple Negative Breast Cancer cohort. Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy. April 2019. [link]
R. Dubois, A. Imbert, A. Samacoits, M. Peter, E. Bertrand, F. Müller, T. Walter. A deep learning approach to identify mRNA localization patterns. Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy. April 2019. [link]

2018

E. Pauwels, F. Bach and J.-P. Vert. Relating leverage scores and density using regularized Christoffel functions. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi and R. Garnett (Eds.), Advances in Neural Information Processing Systems (NeurIPS) 31, 1670-79, 2018. [link]
A. Samacoits, R. Chouaib, A. Safieddine, A.-M. Traboulsi, W. Ouyang, C. Zimmer, M. Peter, E. Bertrand, T. Walter and F. Mueller. A computational framework to study sub-cellular RNA localization. Nature Communications, 9(1), 4584, 2018. [link]
T. Baladi, J. Aziz, F. Dufour, V. Abet, V. Stoven, F. Radvanyi, F. Poyer, T.D. Wu, J.-L. Guerquin-Kern, I. Bernard-Pierrot, S. M. Garrido, S. Piguel. Design, synthesis, biological evaluation and cellular imaging of imidazo[4,5-b]pyridine derivatives as potent and selective TAM inhibitors. Bioorg Med Chem, pii S0968-0896(18)31380-4, 2018. B. Playe, C.-A. Azencott and V. Stoven. Efficient multi-task chemogenomics for drug specificity prediction. PLoS ONE 13(10), 2018. [link]
C.-A. Azencott. Introduction au Machine Learning. Dunod InfoSup, 2018. [link]
N. Servant, N. Varoquaux, E. Heard, J.-P. Vert and E. Barillot. Effective normalization for copy number variation in Hi-C data. BMC Bioinformatics, 19:313, 2018. [link]
P. Naylor, M. Laë, F. Reyal and T. Walter. Segmentation of Nuclei in Histopathology Images by deep regression of the distance map. IEEE Transactions on Medical Imaging, 2018. [link]
C.-A. Azencott. Machine learning and genomics: precision medicine versus patient privacy. Philosophical Transactions of the Royal Society A, 2018. [link]
[arxiv pre-print]
Y. Jiao and J.-P. Vert. The weighted Kendall and high-order kernels for permutations. In J. Dy and A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2314-2322, 2018. [link]
M. Le Morvan and J.-P. Vert. WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models. In J. Dy and A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3635-3644, 2018. [link]
J. Boyd, A. Pinhiero, E. Del Nery, F. Reyal and T. Walter. Analysing Double-Strand Breaks in Cultured Cells for Drug Screening Applications by Causal Inference. Proceedings of the 15th IEEE International Symposium on Biomedical Imaging (ISBI). Washington D.C., United State of America. April, 2018. [link]
K. Vervier, P. Mahé and J.-P. Vert. MetaVW: Large-scale machine learning for metagenomics sequence classification. In Mamitsuka H. (eds), Data Mining for Systems Biology. Methods in Molecular Biology, vol. 1807:9-20, Humana Press, New York, NY, 2018. [link]
Y. Jiao and J.-P. Vert. The Kendall and Mallows kernels for permutations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7):1755-1769, 2018. [link]
E. M. Bunnik, K. B. Cook, N. Varoquaux, G. Batugedara, J. Prudhomme, A. Cort, L. Shi, C. Andolina, L. S. Ross, D. Brady, D. A. Fidock, F. Nosten, R. Tewari, P. Sinnis, F. Ay, J.-P. Vert, W. Noble and K. G. Le Roch. Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages. Nature Communications, 9(1):1910, 2018. [link]
K. Van den Berg, F. Perraudeau, C. Soneson, M. I. Love, D. Risso, J.-P. Vert, M. D. Robinson, S. Dudoit and L. Clement. Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biology, 19:24, 2018. [link]
P. Ruan, M. Hayashida, T. Akutsu and J.-P. Vert. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel. BMC Bioinformatics, 19(Suppl 1):39, 2018. [link]
D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit and J.-P. Vert. ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data. Nature Communications, 9(1):284, 2018. [link]

2017

J.-P. Vert. Quand les algorithmes font parler l'ADN. La Recherche, 529:48-52, 2017. P. Naylor, M. Laé, F. Reyal and T. Walter. Nuclei segmentation in histopathology images using deep neural networks. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, p.933-936, 2017. [link]
J.-L. Plouhinec, S. Medina-Ruiz, C. Borday, E. Bernard, J.-P. Vert, M. B. Eisen, R. M. Harland and A. H. Monsoro-Burq. A molecular atlas of the developing ectoderm defines neural, neural crest, placode and non-neural progenitor identity in vertebrates. PLoS Biology, 15(10): e2004045, 2017. [link]
E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter and J.-P. Vert. Kernel multitask regression for toxicogenetics. Molecular Informatics, 36(10), 2017. [link]
C.-A. Azencott, T. Aittokallio, S. Roy, T. Norman, S. Friend, G. Stolovitzky, A. Goldenberg, and DREAM Idea Challenge Consortium. The inconvenience of data of convenience: computational research beyond post-mortem analyses. Nature Methods 14(10):937-938, 2017. [link]
H. Climente-González, E. Porta-Pardo, A. Godzik and E. Eyras. The Functional Impact of Alternative Splicing in Cancer. Cell Reports, 20(9), 2215–2226, 2017. [link]
M. Le Morvan, A. Zinovyev and J.-P. Vert. NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis. PLoS Computational Biology, 13(6):e1005573, 2017. [link]
H. P. Török, V. Bellon, A. Konrad, M. Lacher, L. Tonenchi, M. Siebeck, S. Brand, E. N. De Toni. Functional Toll-Like Receptor (TLR)2 polymorphisms in the susceptibility to inflammatory bowel disease. PLoS ONE, 12(4):e0175180, 2017. [link]

2016

A.-S. Hamy , H. Bonsang-Kitzis , M. Lae, M. Moarii, B. Sadacca, A. Pinheiro, M. Galliot, J. Abecassis, C. Laurent and F. Reyal. A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in HER2-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways. PLoS ONE 11(12):e0167397, 2016. [link]
N. Tsanov, A. Samacoits, R. Chouaib, A.-M. Traboulsi, T. Gostan, C. Weber, C. Zimmer, K. Zibara, T. Walter, M. Peter, E. Bertrand and F. Mueller. smiFISH and FISH-quant - a flexible single RNA detection approach with super-resolution capability. Nucleic Acids Research, September 2016. [link]
C.-A. Azencott. Network-guided biomarker discovery. In Machine Learning for Health Informatics, Lecture Notes in Computer Science 9605:319-336, 2016. [link]
Y. Jiao, A. Korba and E. Sibony. Controlling the distance to a Kemeny consensus without computing it. Proceedings of the 33rd International Conference on Machine Learning, pp. 2971-2980, New York, NY, USA, 2016. [link]
S. K. Sieberts, F. Zhu [...] L. M. Mangravite. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications 7:12460, 2016. [link]
V. Machairas, T. Baldeweck, T. Walter, E. Decenciere. New General Features Based on Superpixels for Image Segmentation Learning. Proceedings of the 12th IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro. Prague, Czech Republic. April, 2016. [link]
M. Isokane, T. Walter, R. Mahen, B. Nijmeijer, J.-K. Hériché, K. Miura, S. Maffini, M. P. Ivanov, T. S. Kitajima, J.-M. Peters and J. Ellenberg. ARHGEF17 is an essential spindle assembly checkpoint factor that targets Mps1 to kinetochores. Journal of Cell Biology, 212(6): 647-659, 2016. [link]
K. Vervier, P. Mahé, M. Tournoud, J.-B. Veyrieras and J.-P. Vert. Large-scale Machine Learning for Metagenomics Sequence Classification. Bioinformatics, 32(7):1023-1032, 2016. [link]
V. Bellon, V. Stoven and C.-A. Azencott. Multitask feature selection with task descriptors. Pacific Symposium on Biocomputing 21:261-272, 2016 [link]

2015

N. Servant, N. Varoquaux, B. R. Lajoie, E. Viara, C.-J. Chen, J.-P.Vert, E. Heard, J. Dekker and E. Barillot. HiC-Pro: An optimized and flexible pipeline for Hi-C data processing. Genome Biology, 16:259, 2015. [link]
M. Moarii, V. Boeva, J.-P. Vert and F. Reyal. Changes in correlation between promoter regulation and gene expression in cancer. BMC Genomics, 16:873, 2015. [link]
M. Moarii, F. Reyal and J.-P. Vert. Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype. Human Genomics, 9:26, 2015. [link]
N. Shervashidze and F. Bach. Learning the structure for structured sparsity. IEEE Transactions on Signal Processing, 63(18):4894-4902, 2015. [link]
S. Gribkova. Vector quantization and clustering in the presence of censoring. Journal of Multivariate Analysis, 140:220-233, 2015. [link]
L. Guyon, C. Lajaunie, F. Fer, R. Bhajun, E. Sulpice, G. Pinna, A. Campalans, J. P. Radicella, P. Rouillier, M. Mary, S. Combe, P. Obeid, J.-P. Vert and X. Gidrol. Phi-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays. Scientific Reports, 5:14221, 2015. [link]
F. Eduati, L. M. Mangravite et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 33(9):933-940, 2015. [link]
E. Bernard, L. Jacob, J. Mairal, E. Viara and J.-P. Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. BMC Bioinformatics, i16:262, 2015. [link]
Y. Jiao and J.-P. Vert. The Kendall and Mallows Kernels for Permutations. Proceedings of the 32nd International Conference on Machine Learning, JMLR: W&CP 37, 1935-1944, 2015. [link]
N. Varoquaux, I. Liachko, F. Ay, J. Burton, J. Shendure, M. Dunham, J.-P. Vert and W. S. Noble. Accurate identification of centromere locations in yeast genomes using Hi-C. Nucleic Acids Research, 43(11):5331-5339, 2015. [link]
D. G. Grimm, C.-A. Azencott, F. Aicheler, U. Gieraths, D. G. MacArhur, K. E. Samocha, D. N. Cooper, P. D. Stenson, M. J. Daly, J. W. Smoller, L. E. Duncan, K. M. Borgwardt. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human Mutation, 36(5):513-523, 2015. [link]
V. Machairas, M. Faessel, D. Cardenas-Pena, T. Chabardes, T. Walter, E. Decenciere. Waterpixels. IEEE Transactions on Image Processing, 24(11): 3707-3716, Jul 2015. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert and T. Walter. A generic methodological framework for studying single cell motility in high-throughput time-lapse data. Bioinformatics, 31(12):i320-i328, 2015. [link]
E. Scornet, G. Biau and J.-P. Vert. Consistency of random forests. Annals of Statistics, 43(4):1716-1741, 2015. [link]
V. Machairas, E. Decenciere and T. Walter. Spatial Repulsion Between Markers Improves Watershed Performance. Mathematical Morphology and Its Applications to Signal and Image Processing, Lecture Notes in Computer Science, 9082:194-202, 2015. [link]
A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert and T. Walter. Inferring an ontology of single cell motions from high-throughput microscopy data. Proceedings of the 2015 IEEE International Symposium on Biomedical Imaging, 160-163, 2015. [link]
F. Ay, T. H. Vu, M. J. Zeitz, N. Varoquaux, J. E. Carette, J.-P. Vert, A. R. Hoffman and W. S. Noble. Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C. BMC Genomics, 16:121, 2015. [link]
M. Veta, P. J. van Diest, S. M. Willems, H. Wang, A. Madabhushi, A. Cruz-Roa, F. Gonzalez, A. B.L. Larsen, J. S. Vestergaard, A. B. Dahl, D. C. Cireşan, J. Schmidhuber, A. Giusti, L. M. Gambardella, F. Boray Tek, T. Walter, C.-W. Wang, S. Kondo, B. J. Matuszewski, F. Precioso, V. Snell, J. Kittler, T. E. de Campos, A. M. Khan, N. M. Rajpoot, E. Arkoumani, M. M. Lacle, M. A. Viergever, J. P.W. Pluim. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis, 20(1):237-248, 2015. [link]
R. Bhajun, L. Guyon, A. Pitaval, E. Sulpice, S. Combe, P. Obeid, V. Haguet, I. Ghorbel, C. Lajaunie and X. Gidrol. A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator. Scientific Reports, 5:8336, 2015. [link]
F. Ay, E. Bunnik, N. Varoquaux, J.-P. Vert, W. S. Noble and K. Le Roch. Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum. BioEssays, 37(2):182-194, 2015. [link]

2014

V Graml, X Studera, JLD Lawson, A Chessel, M Geymonat, M Bortfeld-Miller, T Walter, L Wagstaff, E Piddini and RE Carazo-Salas. A Genomic Multiprocess Survey of Machineries that Control and Link Cell Shape, Microtubule Organization, and Cell-Cycle Progression. Developmental Cell , 31(2):227-239, 2014. [link]
V. Machairas, T. Walter, E. Decenciere. Waterpixels: Superpixels based on the watershed transformation. IEEE International Conference on Image Processing (ICIP) p. 4343 - 4347, November 2014. J Tegha-Dunghu, E Bausch, B Neumann, A Wuensche, T Walter, J Ellenberg, OJ Gruss. MAP1S controls microtubule stability throughout the cell cycle in human cells. Journal of Cell Science, 127:5007-5013, 2014. [link]
E. Richard, G. Obozinski and J.-P. Vert. Tight convex relaxations for sparse matrix factorization. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, 3284-3292, 2014. [link]
J.-K. Hériché, J.G. Lees, I. Morilla, T. Walter, B. Petrova, M.J. Roberti, M.J. Hossain, P. Adler, J.M. Fernández, M. Krallinger, C.H. Haering, J. Vilo, A. Valencia, J.A. Ranea, C. Orengo, J. Ellenberg. Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation. Molecular Biology of the Cell, 25:2522–2536, Aug 2014. [link]
M. Moarii, A. Pinheiro, B. Sigal-Zafrani, A. Fourquet, M. Caly, N. Servant, V. Stoven J.-P. Vert and F. Reyal. Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences. PLoS ONE, 9(8):e103986, 2014. [link]
K. Vervier, P. Mahé, A. d'Aspremont, J.-B. Veyrieras and J.-P. Vert. On learning matrices with orthogonal columns or disjoint supports. In T. Calder et al. (Eds), ECML PKDD 2014, Part III, LNCS 8726, 274-289, Springer-Verlag Berlin Heidelberg, 2014. [link]
J. C. Costello, L. M. Heiser, E. Georgii, M. Gönen, M. P Menden, N. J Wang, M. Bansal, M. Ammad-ud-din, P. Hintsanen, S. A Khan, J.-P. Mpindi, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, NCI DREAM Community, J. J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J. W. Gray and G. Stolovitzky. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32:1202-1212, 2014. [link]
E. Bernard, L. Jacob, J. Mairal and J.-P. Vert. Efficient RNA isoform identification and quantification from RNA-seq data with network flows.Bioinformatics, 30(17):2447-2455, 2014. [link]
F. Ay, E. M. Bunnik, N. Varoquaux, S. M. Bol, J. Prudhomme, J.-P. Vert, W. S. Noble and K. G. Le Roch. Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression. Genome Research, 24:974-988, 2014. [link]
C. Tourette, F. Farina, R. P. Vazquez-Manrique, A.-M. Orfila, S. Hernandez, N. Offner, J. A. Parker, S. Menet, J. Kim, J. Lyu, S. H. Choi, K. Cormier, C. K. Edgerly, O. L. Bordiuk, K. Smith, A. Louise, M. Halford, S. Stacker, J.-P. Vert, R. J. Ferrante, W. Lu and C. Neri. Increase in the Wnt receptor Ryk promotes the early stage decline of mutant polyglutamine neurons by repressing FOXO protective activity. PLoS Biology, 12(6):e1001895, 2014. [link]
E. Pauwels, C. Lajaunie and J.-P. Vert. A Bayesian active learning strategy for sequential experimental design in systems biology. BMC Systems Biology, 8:102, 2014. [link]
N. Varoquaux, F. Ay, W. S. Noble and J.-P. Vert. A statistical approach for inferring the three-dimensional structure of the genome. Bioinformatics, 30(12):i26-i33, 2014. [link]
T. D. Hocking, V. Boeva, G. Rigaill, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre, W. Richer, F. Bourdeaut, M. Suguro, M. Seto, F. Bach and J.-P. Vert. SegAnnDB: interactive web-based genomic segmentation. Bioinformatics, 30(11):1539-1546, 2014. [link]
J.-L. Plouhinec, D. D. Roche, C. Pegoraro, A.-L. Figueiredo, F. Maczkowiak, L. J. Brunet, M. Cecile, J.-P. Vert, N. Pollet, R. M. Harland and A.-H. Monsoro-Burq. Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers. Developmental Biology, 386(2):461-472, 2014. [link]
F. Mordelet and J.-P. Vert, "A bagging SVM to learn from positive and unlabeled examples. Pattern Recognition Letters, 37:201-209, 2014. [link]

2013

Pau, G., Walter T., Neumann B., Hériché H.-K., Ellenberg J., Huber W. Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay BMC Bioinformatics, 14(1):308, Oct 2013. [link]
T. Traoré, A. Cavagnino, N. Saettel, F. Radvanyi, S. Piguel, I. Bernard-Pierrot, V. Stoven, M. Legraverend. New aminopyrimidine derivatives as inhibitors of the TAM family. European Journal of Medicinal Chemistry, 70:789-801, 2013 [link]
A. Bouillon, D. Giganti, C. Benedet, O. Gorgette, S. Pêtres, E. Crublet, C. Girard-Blanc, B. Witkowski, D. Ménard, M. Nilges, O. Mercereau-Puijalon, V. Stoven, J.C. Barale. In silido screening on the three-dimensional model of the plasmodium vivax SUB1 protease leads to the validation of a novel anti-parasite compound. Journal of Biological Chemistry, 288: 18561-18573, 2013. [link]
Y. Zhao, T. Tamura, T. Akutsu and J-P. Vert. Flux balance impact degree: A new definition of impact degree to properly treat reversible reactions in metabolic networks. Bioinformatics, 29(17):2178-2185, 2013. [link]
T. D. Hocking, G. Schleiermacher, I. Janoueix-Lerosey, V. Boeva, J. Cappo, O. Delattre, F. Bach and J.-P. Vert. Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinformatics, 14:164, 2013. [link]
E. Richard, F. Bach and J.-P. Vert. Intersecting singularities for multi-structured estimation. In S. Dasgupta and D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28(3):1157-1165, 2013. [link]
G. Rigaill, T. D. Hocking, F. Bach and J.-P. Vert. Learning sparse penalties for change-point detection using max margin interval regression. In S. Dasgupta and D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28(3):172-180, 2013. [link]
R.M Suarez, F. Chevot, A. Cavagnino, N. Saettel, F. Radvanyi, S. Piguel, I. Bernard-Pierrot, V. Stoven, M. Legraverend. Inhibitors of the TAM subfamily of tyrosine kinases: Synthesis and biological evaluation. European Journal of Medicinal Chemistry, 61:2-25, 2013. [link]
F. Mordelet and J.-P. Vert. Supervised inference of gene regulatory networks from positive and unlabeled examples. In H. Mamitsuka, C. DeLisi and M. Kanehisa (Eds), Data Mining for Systems Biology, Methods in Molecular Biology 939, Humana Press, p.47-58, 2013. [link]

2012

N. Servant, M. A. Bollet, H. Halfwerk, K. Bleakley, B. Kreike, L. Jacob, D. Sie, R. M. Kerkhoven, P. Hupé, R. Hadhri, A. Fourquet, H. Bartelink, E. Barillot, B. Sigal-Zafrani and M. J. van de Vijver. Search for a Gene Expression Signature of Breast Cancer Local Recurrence in Young Women. Clinical Cancer Research, 18(6):1704-1715, 2012. [link]
A.-C. Haury, F. Mordelet, P. Vera-Licona and J.-P. Vert. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Sytems Biology, 6:145, 2012. [link]
O. Filhol, D. Ciais, C. Lajaunie, P. Charbonnier, N. Foveau, J.-P. Vert and Y. Vandenbrouck. DSIR: Assessing the design of highly potent siRNA by testing a set of cancer-relevant target genes. PLoS ONE, 7(10):e48057 [link]
. Mall, M., Walter, T., Gorjánácz, M., Davidson, I.F., Ly-Hartig, N., Ellenberg, J., & Mattaj, I.W. Mitotic lamin disassembly is triggered by lipidmediated signaling. Journal of Cell Biology, 198(6):981-990, 2012. [ link]
S. Mizutani, E. Pauwels, V. Stoven, S. Goto, and Y. Yamanishi. Relating drug-protein interaction network with drug side-effects. Bioinformatics, 28(18):i522-i528, 2012. [link]
Y. Tabei, E. Pauwels, V. Stoven, K. Takemoto, and Y. Yamanishi. Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinformatics, 28(18):i487-i494, 2012. [link]
E. Pauwels, D. Surdez, G. Stoll, A. Lescure, E. Del Nery, O. Delattre, V. Stoven. A Probabilistic Model for Cell Population Phenotyping Using HCS Data. PLoS ONE, 7(8):e42715, 2012. [link]
D. Marbach, J.C. Costello, R. Kuffner, N. Vega, R.J. Prill, D.M. Camacho, K.R. Allison, the DREAM5 Consortium, M. Kellis, J.J. Collins, G. Stolovitzky. Wisdom of crowds for robust gene network inference. Nature Methods, 9:796-804, 2012. [link]
C. Houdayer, V. Caux-Moncoutier, S. Krieger, M. Barrois, F. Bonnet, V. Bourdon, M. Bronner, M. Buisson, F. Coulet, P. Gaildrat, C. Lefol, M. Léone, S. Mazoyer, D. Muller, A. Remenieras, F. Révillion, E. Rouleau, J. Sokolowska, J.-P. Vert, R. Lidereau, F. Soubrier, H. Sobol, N. Sevenet, B. Bressac de Paillerets, A. Hardouin, M. Tosi, O.M. Sinilnikova and D. Stoppa-Lyonnet. Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants. Human Mutation, 33(8):1228-1238, 2012. [link]
F. Lejeune, L. Mesrob, F. Parmentier, C. Bicep, R. Vazquez, A. Parker, J.-P. Vert, C. Tourette and C. Neri. Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons. BMC Genomics, 13:91, 2012. [link]
V. Boeva, T. Popova, K. Bleakley, P. Chiche, J. Cappo, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre and E. Barillot. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics, 28(3):423-425, 2010. [link]
K. Takemoto, T. Tamura, Y. Cong, W.-K. Ching, J.-P. Vert and T. Akutsu. Analysis of the impact degree distribution in metabolic networks using branching process approximation. Physica A, 391(1-2):379-387, 2012. [link]

2011

A.-C. Haury, P. Gestraud and J.-P. Vert, "The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures", PLoS ONE, 6(12):e28210, 2011. [link]
F. Mordelet and J.-P. Vert, "ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples", BMC Bioinformatics, 12:389, 2011. [link]
T. Matsui, M. Goto, J.-P. Vert and Y. Uchiyama, "Gradient-based musical feature extraction based on scale-invariant feature transform", in Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011), p.724-728, 2011. [pdf]
T. Hocking, A. Joulin, F. Bach and J.-P. Vert, "Clusterpath: an algorithm for clustering using convex fusion penalties", in L. Getoor and T. Scheffer (Eds.), Proceedings of the 28th International Conference on Machine Learning (ICML-11), p.745-752, ACM, New-York, NY, USA, 2011. [pdf]
E. Pauwels, V. Stoven, and Y. Yamanishi, "Predicting drug side-effect profiles: a chemical fragment-based approach", BMC Bioinformatics, 12:169, 2011. [link]
Y. Yamanishi, E. Pauwels, H. Saigo, and V. Stoven, "Extracting sets of chemical substructures and protein domains governing drug-target interactions", Journal of Chemical Information and Modeling, 51 (5), pp 1183–1194, 2011. [link]
V. Boeva, A. Zinovyev, K. Bleakley, J.-P. Vert, I. Janoueix-Lerosey, O. Delattre and E. Barillot, "Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization", Bioinformatics 27(2):268-269, 2011. [link]

2010

K. Bleakley and J.-P. Vert, "Fast detection of multiple change-points shared by many signals using group LARS", in J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel and A. Culotta (Eds), Advances in Neural Information Processing Systems 23 (NIPS), p.2343-2351, 2010. [pdf]
M. Zaslavskiy, F. Bach and J.-P. Vert, "Many-to-many graph matching: a continuous relaxation approach", in J. Balcazar, F. Bonchi and A. Gionis (Eds.), Machine Learning and Knowledge Discovery in Databases (Proceedings of ECML/PKDD 2010), LNCS 6323, p.515-530, Springer, 2010. [link]
T. Tamura, Y. Yamanishi, M. Tanabe, S. Goto, M. Kanehisa, K. Horimoto, and T. Akutsu, "Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer", Genome Informatics (Proceedings of IBSB2010), Vol.24, pp.193-203, 2010. H. Lodhi and Y. Yamanishi (Eds.), Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, IGI Global, 2010 [link]
Y. Yamanishi and H. Kashima, "Prediction of compound-protein interactions with machine learning methods", in H. Lodhi and Y. Yamanishi (Eds.), Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, p.304-317, IGI Global, 2010. J.-P. Vert, "3D ligand-based virtual screening with support vector machines", in H. Lodhi and Y. Yamanishi (Eds.), Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, p.35-45, IGI Global, 2010. K. Schauer, T. Duong, K. Bleakley, S. Bardin, M. Bornens and B. Goud, "Construction of probabilistic density maps for micropatterned cells: a novel method to study the global cellular architecture of endomembranes", Nature Methods, 7, p. 560–566, 2010. H. Kashima, S. Oyama, Y. Yamanishi and K. Tsuda, "Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel", IEICE Trans Inf Syst, E93D(10):2672-2679, 2012. M. Hue and J.-P. Vert, "On learning with kernels for unordered pairs", in J. Furnkranz and T. Joachims (Eds.), Proceedings of the 27th International Conference on Machine Learning (ICML), p.463-470, 2010. [pdf]
Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, "Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework", Bioinformatics (Proceedings of ISMB2010), 26: i246-i254, 2010. [link]
G. Biau, K. Bleakley, L. Györfi and G. Ottucsák, "Nonparametric sequential prediction of time series", Journal of Nonparametric Statistics, 22(3), p. 297-317, 2010. M. Hue, M. Riffle, J.-P. Vert and W.S. Noble,"Large-scale prediction of protein-protein interactions from structures", BMC Bioinformatics, 11:144, 2010. B. Hoffmann, M. Zaslavskiy, J.-P. Vert and V. Stoven, "A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction", BMC Bioinformatics, 11:99, 2010. Y. Yamanishi, "Supervised inference of metabolic networks from the integration of genomic data and chemical information", in H. Lodhi and S. Muggleton (Eds.), Elements of Computational Systems Biology, Wiley, p.189-212, 2010. J.-P. Vert, "Reconstruction of biological networks by supervised machine learning approaches", in H. Lodhi and S. Muggleton (Eds.), Elements of Computational Systems Biology, Wiley, p.165-188, 2010.

2009

M. Cuturi, J.-P. Vert and A. d'Aspremont, "White Functionals for Anomaly Detection in Dynamical Systems", in Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams and A. Culotta (Eds), Advances in Neural Information Processing Systems 22 (NIPS), p.432-440, 2009. [pdf]
F. Austerlitz, O. David, B. Schaeffer, K. Bleakley, M. Olteanu, R. Leblois, M. Veuille and C. Laredo, "DNA barcode analysis: a comparison of phylogenetic and statistical classification methods", BMC Bioinformatics, 10(Suppl 14):S10, 2009. [link]
H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama, and K. Tsuda, "Simultaneous Inference of Biological Networks of Multiple Species from Genome-wide Data and Evolutionary Information: A Semi-supervised Approach", Bioinformatics, Vol.25, p.2962-2968, 2009. [link]
Duclert-Savatier N., Poggi L., Lopes P., Chevalier N., Nilges M., Delarue M. and Stoven V. "Insights into the enzymatic mechanism of 6-phosphogluconolactonase from Trypanosoma brucei using structural data and molecular dynamics simulation". Journal of Molecular Biology, 388(5):1009-21, 2009. M. Zaslavskiy, M. Dymetman and N. Cancedda "Phrase-Based Statistical Machine Translation as a Traveling Salesman Problem", Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2009), p.333-341, 2009. M. Zaslavskiy, F. Bach and J.-P. Vert, "A path following algorithm for the graph matching problem", IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2227-2242, 2009. [link]
J.-P. Vert, T. Matsui, S. Satoh and Y. Uchiyama, "High-level feature extraction using SVM with walk-based graph kernel", Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p. 1121-1124, 2009. L. Jacob, G. Obozinski, J.-P. Vert, "Group Lasso with Overlaps and Graph Lasso", in L. Bottou and M. Littman, (Ed.), Proceedings of the 26th International Conference on Machine Learning, 433-440, 2009. [link]
P. Mahé and J.-P. Vert, "Virtual screening with support vector machines and structure kernels", Combinatorial Chemistry & High Throughput Screening, 12(4):409-423, 2009. [link]
K. Bleakley and Y. Yamanishi, "Supervised prediction of drug-target interactions using bipartite local models", Bioinformatics, Vol.25, pp.2397-2403, 2009. [link]
M. Zaslavskiy, F. Bach and J.-P. Vert, "Global alignment of protein-protein interaction networks by graph matching methods", Bioinformatics (Proceedings of ISMB/ECCB2009), Vol.25, pp.i259-1267, 2009. [link]
Y. Yamanishi, M. Hattori, M. Kotera, S. Goto, and M. Kanehisa, "E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs", Bioinformatics (Proceedings of ISMB/ECCB2009), Vol.25, pp.i179-i186, 2009. [link]
H. Kashima, S. Oyama, Y. Yamanishi, and K. Tsuda, "On Pairwise Kernels: An Efficient Alternative and Generalization Analysis", Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vol.5476, pp.1030-1037, 2009. [link]
H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, and K. Tsuda, "Link Propagation: A Fast-semi Supervised Learning Algorithm for Link Prediction", Proceedings of the 9th SIAM Conference on Data Mining (SDM), pp.1099-1110, 2009. [link]
Y. Yamanishi, "Supervised Bipartite Graph Inference", Advances in Neural Information Processing Systems 21, D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), pp.1841-1848, MIT Press, Cambridge, MA, 2009. [link]
L. Jacob, F. Bach and J.-P. Vert, "Clustered multitask learning: a convex formulation", Advances in Neural Information Processing Systems 21, D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), pp.745-752, MIT Press, Cambridge, MA, 2009. [pdf]
J. Abernethy, F. Bach, T. Evgeniou and J.-P. Vert, "A new approach to collaborative filtering: operator estimation with spectral regularization", Journal of Machine Learning Research, 10:803-826, 2009. [link]
P. Mahé and J.-P. Vert, "Graph kernels based on tree patterns for molecules", Machine Learning, 75(1):3-35, 2009. [link]

2008

K. Bleakley, M.-P. Lefranc and G. Biau, "Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analysed with IMGT tools", BMC Bioinformatics, 9:408, 2008. [link]
T.E. Malliavin, H. Munier-Lehman, V. Stoven. "Virtual screening of the guanylate Monophosphate Kinase (GMPK) family: investigating the rules of ligand specificity." Letters in Drug Design & Discovery, 5(5):319-326. [link]
L. Jacob and J.-P. Vert, "Protein-ligand interaction prediction: an improved chemogenomics approach", Bioinformatics, 24(19):2149-2156, 2008. [link]
L. Jacob, B. Hoffmann, V. Stoven and J.-P. Vert, "Virtual screening of GPCRs: an in silico chemogenomics approach", BMC Bioinformatics, 9:363, 2008. [link]
J.-P. Vert and L. Jacob, "Machine learning for in silico virtual screening and chemical genomics: new strategies", Combinatorial Chemistry & High Throughput Screening, 11(8):677-685, 2008. [link]
F. Mordelet and J.-P. Vert, "SIRENE: Supervised Inference of REgulatory NEtworks", Bioinformatics, 24(16):i76-i82, 2008. [link]
F. Rapaport, E. Barillot and J.-P. Vert, "Classification of arrayCGH data using a fused SVM", Bioinformatics, 24(13):i365-i382, 2008. [link]
Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda and M. Kanehisa, "Prediction of drug-target interaction networks from the integration of chemical and genomic spaces", Bioinformatics, 24(13):i232-240, 2008. [link]
M. Zaslavskiy, F. Bach and J.-P. Vert, "A path following algorithm for graph matching", in A. Elmoataz, O. Lezoray, F. Nouboud and D. Mammass (Eds.), Proceedings of the 3rd International Conference on Image and Signal Processing (ICISP 2008), LNCS 5099:329-337, 2008. [link]
L. Jacob and J.-P. Vert, "Efficient peptide-MHC-I binding prediction for alleles with few known binders", Bioinformatics, 24(3):358-366, 2008 [link]
[server]
J. Abernethy, T. Evgeniou, O. Toubia and J.-P. Vert, "Eliciting consumer preferences using robust adaptive choice questionnaires", IEEE Transactions on Knowledge and Data Engineering, 20(2):145-155, 2008. [link]

2007

J.-P. Vert, J. Qiu and W. S. Noble, "A new pairwise kernel for biological network inference with support vector machines", BMC Bioinformatics, 8(Suppl 10):S8, 2007. [link]
K. Bleakley, G. Biau and J.-P. Vert, "Supervised network inference with local models", Bioinformatics, 23(13):i57-i65, 2007. [link]
M. Cuturi, J.-P. Vert, O. Birkenes and T. Matsui, "A kernel for time series based on global alignments", Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) , 2:.II-413-II-416, 2007. [link]
Y. Yamanishi, F. Bach and J.-P. Vert, "Glycan classification with tree kernels", Bioinformatics, 23(10):1211-1216, 2007 [link]
. J. Qiu, M. Hue, A. Ben-Hur, J.-P. Vert and W. S. Noble, "A structural alignment kernel for protein structures", Bioinformatics 23(9):1090-1098, 2007[link]
. Y. Yamanishi and J.-P. Vert, "Kernel matrix regression", Proceedings of the 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007), 2007. J.-P. Vert, "Kernel methods in genomics and computational biology", in Camps-Valls, G., Rojo-Alvarez, J.-L. and Martinez-Ramon, M. (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing, p.42-63, Idea Group, 2007. F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot and J.-P. Vert, '"Classification of microarray data using gene networks", BMC Bioinformatics, 8:35, 2007. [link]
M. Delarue, N. Duclert-Savatier, E. Miclet, A. Haouz, D. Giganti, J. Ouazzani, P. Lopez, M. Nilges and V. Stoven, "Three dimensional structure and implications for the catalytic mechanism of 6-phosphogluconolactonase from Trypanosoma brucei", Journal of Molecular Biology, 306(3):868-881, 2007. [link]
F. Lemaire, C. A. Mandon, J. Reboud, A. Papine, J. Angulo, H. Pointu, C. Diaz-Latoud, C. Lajaunie, F. Chatelain, A.-P. Arrigo and B. Schaack, "Toxicity assays in nanodrops combining bioassay and morphometric endpoints", PLoS ONE, 2(1):e163, 2007 [link]

2006

Y. Yamanishi and J.P. Vert, "Estimating Protein Network from Multiple Genomic Data by Kernel Methods", Proceedings of the Institute of Statistical Mathematics, 54(2):357:373, 2006 [pdf]
J.-P. Vert, N. Foveau, C. Lajaunie and Y. Vandenbrouck, "An accurate and interpretable model for siRNA efficacy prediction", BMC Bioinformatics, 7:520, 2006. [link]
F. Carrat , J. Luong , H. Lao , A.-V. Salle , C. Lajaunie and H. Wackernage, "A "small-world-like" model for comparing interventions aimed at preventing and controlling influenza pandemics", BMC Medicine, 4:26, 2006 [link]
Y. Yamanishi and Y. Tanaka, "Sensitivity Analysis in Kernel Principal Component Analysis", in A. Rizzi and M. Vichi (Eds.), COMPSTAT 2006 - Proceedings in Computational Statistics, p. 787-794, Physica-Verlag/Springer, 2006. P. Mahé, L. Ralaivola, V. Stoven and J.-P. Vert, "The pharmacophore kernel for virtual screening with support vector machines", Journal of Chemical Information and Modeling, 46(5):2003-2014, 2006. [link]
J.-P. Vert, "Classification of biological sequences with kernel methods", in Sakakibara et al. (Eds.), Proceedings of ICGI 2006, LNAI 4201, p.7-18, Springer Verlag, 2006. K. Loth, D. Abergel, P. Pelupessy, M. Delarue, P. Lopes, J. Quazzani, N. Duclert-Savatier, M. Nilges, G. Bodenhausen and V. Stoven, "Determination of hihedral Y angles in large proteins by combining NH/CaHa dipole/dipole cross correlation and chemical shifts", Proteins: Structure, Function, and Bioinformatics, 64(4):931-939, 2006. [link]
R. Vert and J.-P. Vert, "Consistency and convergence rates of one-class SVM and related algorithms", Journal of Machine Learning Research, 7:817-854, 2006. [link]
H. Saigo, J.-P. Vert and T. Akutsu, "Optimizing amino acid substitution matrices with a local alignment kernel", BMC Bioinformatics 7:246, 2006. [link]
J.-P. Vert, R. Thurman and W. S. Noble, "Kernels for gene regulatory regions", Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf and J. Platt (Eds.), p.1401-1408, MIT Press, Cambridge, MA, 2006. [pdf]
R. Vert and J.-P. Vert, "Consistency and convergence rates of one-class SVM and related algorithms", Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf and J. Platt (Eds.), p.1409-1416, MIT Press, Cambridge, MA, 2006. [pdf]

2005

J. Vermorel and M. Mohry, "Multi-armed Bandit Algorithm and Empirical Evaluation", in Proceedings of the 16th European Conference of Machine Learning (ECML'05), vol. 3720 of Lecture Notes in Computer Science, p. 437-448, Springer, Heidelberg, Germany, 2005. [link]
. S. Matsuda, J.-P. Vert, H. Saigo, N. Ueda, H. Toh, and T. Akutsu, "A novel representation of protein sequences for prediction of subcellular location using support vector machines", Protein Science, vol. 14, p. 2804-2813, 2005. [link]
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M. Cuturi and J.-P. Vert, "The context-tree kernel for strings", Neural networks, vol. 18, n. 4, p. 1111-1123, 2005. [link]
T. Sato, Y. Yamanishi, M. Kanehisa and H. Toh, "The inference of protein–protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships", Bioinformatics, vol. 21(17), p.3482-3489, 2005. [link]
M. Cuturi, K. Fukumizu and J.-P. Vert, "Semigroup kernels on measures", Journal of Machine Learning Research, vol. 6, p. 1169-1198, 2005. [link]
Y. Yamanishi, J.-P. Vert and M. Kanehisa, "Supervised enzyme network inference from the integration of genomic data and chemical information", Bioinformatics, vol. 21, p. i468-i477, 2005. [link]
P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and J.-P. Vert, "Graph kernels for molecular structure-activity relationship analysis with support vector machines", J. Chem. Inf. Model., vol. 45, n. 4, 939 -951, 2005. [link]
M. Cuturi and J.-P. Vert, "Semigroup kernels on finite sets", Advances in Neural Information Processing Systems 17, Lawrence K. Saul and Yair Weiss and Léon Bottou (Eds.), p.329-336, MIT Press, Cambridge, MA, 2005. [pdf]
J.-P. Vert and Y. Yamanishi, "Supervised graph inference", Advances in Neural Information Processing Systems 17, Lawrence K. Saul, Yair Weiss and Léon Bottou (Eds.), p.1433-1440, MIT Press, Cambridge, MA, 2005. [pdf]

2004

P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret and J.-P. Vert, "Extensions of marginalized graph kernels", Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), R. Greiner and D. Schuurmans (Eds.), p.552-559, ACM Press, 2004. [pdf]
M. Cuturi, J.-P. Vert, "A mutual information kernel for strings";, Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, p. 1905-1910, 2004. [pdf]
Y. Yamanishi, J.-P. Vert and M. Kanehisa, "Protein network inference from multiple genomic data: a supervised approach", Bioinformatics, vol.20, p.i363-i370, 2004. [link]
B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), "Kernel Methods in Computational Biology", MIT Press, 2004. [link]
J.-P. Vert, K. Tsuda and B. Schölkopf, "A primer on kernel methods", in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.35-70, 2004. J.-P. Vert, H. Saigo, T. Akutsu, "Local alignment kernels for biological sequences", in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.131-154, 2004. R. Kondor and J.-P. Vert, "Diffusion kernels", in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.171-192, 2004. Y. Yamanishi, J.-P. Vert and M. Kanehisa, "Heterogeneous data comparison and gene selection with kernel canonical correlation analysis", in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.209-230, 2004. H. Saigo, J.-P. Vert, T. Akutsu and N. Ueda, "Protein homology detection using string alignment kernels", Bioinformatics, vol.20, p.1682-1689, 2004. [link]

2003

J.-P. Vert and M. Kanehisa, "Extracting active pathways from gene expression data", Bioinformatics, vol. 19, p. 238ii-244ii, 2003. [link]
Y. Yamanishi, J.-P. Vert, A. Nakaya and M. Kanehisa, "Extraction of Correlated Gene Clusters from Multiple Genomic Data by Generalized Kernel Canonical Correlation Analysis", Bioinformatics, vol. 19, p. 323i-330i, 2003. [link]
J.-P. Vert and M. Kanehisa, "Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA", Advances in Neural Information Processing Systems 15, Suzanna Becker, Sebastian Thrun and Klaus Obermayer (Eds), p. 1425-1432, MIT Press, Cambridge, MA, 2003. [pdf]

2002

J.-P. Vert, "A tree kernel to analyze phylogenetic profiles", Bioinformatics, vol. 18, p. S276-S284, 2002. [link]
J.-P. Vert, "Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings", Proceedings of the Pacific Symposium on Biocomputing 2002, Altman, R.B., Dunker, A.K., Hunter, L., Lauerdale, K. and Klein, T.E., (Ed.), World Scientific, pp. 649-660, 2002. [pdf]