Jean-Philippe Vert

Jean-Philippe Vert

Professor, Chief Research and Development Officer at Owkin

Publications

You can also find my articles on my Google Scholar profile.

  1. L. Meng-Papaxanthos, R. Zhang, G. Li, M. Cuturi, W. S. Noble, & J.-P. Vert. LSMMD-MA: scaling multimodal data integration for single-cell genomics data analysis. Bioinformatics, 2023.
  2. J.-P. Vert. How will generative ai disrupt data science in drug discovery? Nat. Biotechnol., 2023.
  3. L. Stewart, F. R. Bach, Q. Berthet, & J.-P. Vert. Regression as classification: influence of task formulation on neural network features. In F. Ruiz, J. Dy, & J.-W. van de Meent (Eds), Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, 11563–11582. PMLR, 2023.
  4. G. Baid, D. E. Cook, K. Shafin, T. Yun, F. Llinares-López, Q. Berthet, … A. Carroll. DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer. Nat. Biotechnol., 41:232–238, 2023.
  5. N. Varoquaux, W. S. Noble, & J.-P. Vert. Inference of genome 3D architecture by modeling overdispersion of Hi-C data. Bioinformatics, 39(1):btac838, 2023.
  6. F. Llinares-López, Q. Berthet, M. Blondel, O. Teboul, & J.-P. Vert. Deep embedding and alignment of protein sequences. Nat. Methods, 20:104–111, 2023.
  7. F. Raimundo, P. Prompsy, J.-P. Vert, & C. Vallot. A benchmark of computational pipelines for single-cell histone modification data. Genome Biol., 2023.
  8. M. Blondel, Q. Berthet, M. Cuturi, R. Frostig, S. Hoyer, F. Llinares-López, … J.-P. Vert. Efficient and modular implicit differentiation. In Advances in Neural Information Processing Systems (NeurIPS 2022), volume 35. 2022.
  9. R. Zhang, L. Meng-Papaxanthos, J.-P. Vert, & W. S. Noble. Multimodal single-cell translation and alignment with semi-supervised learning. J. Comput. Biol., 29:1198–1212, 2022.
  10. L. Carratino, M. Cissé, R. Jenatton, & J.-P. Vert. On mixup regularization. J. Mach. Learn. Res., 23(325):1–31, 2022.
  11. P. Marion, A. Fermanian, G. Biau, & J.-P. Vert. Scaling ResNets in the large-depth regime. Technical Report arXiv 2206.06929, 2022.
  12. R. Zhang, L. Meng-Papaxanthos, J.-P. Vert, & W. S. Noble. Semi-supervised single-cell cross-modality translation using Polarbear. In I. Pe'er (Ed), International Conference on Research in Computational Molecular Biology (RECOMB 2022), volume 13278 of Lecture Notes in Computer Science, 20–35. Springer International Publishing, 2022.
  13. V. Mallet, & J.-P. Vert. Reverse-complement equivariant networks for DNA sequences. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, & J. W. Vaughan (Eds), Advances in Neural Information Processing Systems (NeurIPS 2021), volume 34, 13511–13523. Curran Associates, Inc., 2021.
  14. A. Fermanian, P. Marion, J.-P. Vert, & G. Biau. Framing RNN as a kernel method: a neural ODE approach. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, & J. W. Vaughan (Eds), Advances in Neural Information Processing Systems (NeurIPS 2021), volume 34, 3121–3134. Curran Associates, Inc., 2021.
  15. J. Abécassis, F. Reyal, & J.-P. Vert. CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data. Nat. Commun., 12:5352, 2021.
  16. F. Raimundo, L. Meng-Papaxanthos, C. Vallot, & J.-P. Vert. Machine learning for single-cell genomics data analysis. Curr. Opin. Syst. Biol., 26:64–71, 2021.
  17. M. Blondel, A. Mensch, & J.-P. Vert. Differentiable divergences between time series. In A. Banerjee, & K. Fukumizu (Eds), Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), volume 130, 3853–3861. PMLR, 2021.
  18. M. Pascucci, G. Royer, J. Adamek, M. A. Asmar, D. Aristizabal, L. Blanche, … M.-A. Madoui. AI-based mobile application to fight antibiotic resistance. Nat. Commun., 12:1173, 2021.
  19. Q. Berthet, M. Blondel, O. Teboul, M. Cuturi, J.-P. Vert, & F. R. Bach. Learning with differentiable perturbed optimizers. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, & H. Lin (Eds), Advances in Neural Information Processing Systems (NeurIPS 2020), volume 33, 9508–9519. Curran Associates, Inc., 2020.
  20. L. Slim, C. Chatelain, C.-A. Azencott, & J.-P. Vert. Novel methods for epistasis detection in genome-wide association studies. PLoS One, 15:e0242927, 2020.
  21. B. Colnet, I. Mayer, G. Chen, A. Dieng, R. Li, G. Varoquaux, … S. Yang. Causal inference methods for combining randomized trials and observational studies: a review. Technical Report arXiv 2011.08047, 2020.
  22. P.-C. Aubin-Frankowski, & J.-P. Vert. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics, 36:4774–4780, 2020.
  23. F. Raimundo, C. Vallot, & J.-P. Vert. Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol., 21:212, 2020.
  24. M. Cuturi, O. Teboul, J. Niles-Weed, & J.-P. Vert. Supervised quantile normalization for low rank matrix factorization. In H.D. III, & A. Singh (Eds), Proceedings of the 37th International Conference on Machine Learning (ICML 2020), volume 119 of Proceedings of Machine Learning Research, 2269–2279. PMLR, 2020.
  25. M. Cuturi, O. Teboul, & J.-P. Vert. Noisy adaptive group testing using bayesian sequential experimental design. Technical Report arXiv 2004.12508, 2020.
  26. K. B. Cook, B. H. Hristov, K. G. Le Roch, J.-P. Vert, & W. S. Noble. Measuring significant changes in chromatin conformation with ACCOST. Nucleic Acids Res., 48(5):2303–2311, 2020.
  27. R. Menegaux, & J.-P. Vert. Embedding the de Bruijn graph, and applications to metagenomics. Technical Report bioRxiv 2020.03.06.980979, 2020.
  28. J.-P. Vert. Artificial intelligence and cancer genomics. In B. Nordlinger, C. Villani, & D. Rus (Eds), Healthcare and Artificial Intelligence, pages 165–174. Springer, Cham., 2020.
  29. M.-M. Aynaud, O. Mirabeau, N. Gruel, S. Grossetête, V. Boeva, S. Durand, … A. Zinovyev. Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution. Cell Rep., 30:1767–1779.e6, 2020.
  30. I. Mayer, J. Josse, F. Raimundo, & J.-P. Vert. MissDeepCausal: causal inference from incomplete data using deep latent variable models. Technical Report arXiv 2002.10837, 2020.
  31. M. Cuturi, O. Teboul, & J.-P. Vert. Differentiable ranking and sorting using optimal transport. In H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E.B. Fox, & R. Garnett (Eds), Advances in Neural Information Processing Systems (NeurIPS 2019), volume 32, 6858–6868. Curran Associates, Inc., 2019.
  32. J. Abécassis, A.-S. Hamy, C. Laurent, B. Sadacca, H. Bonsang-Kitzis, F. Reyal, & J.-P. Vert. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data. PLoS One, 14:e0224143, 2019.
  33. A. Genevay, G. Dulac-Arnold, & J.-P. Vert. Differentiable deep clustering with cluster size constraints. Technical Report arXiv 1910.09036, 2019.
  34. O. Collier, V. Stoven, & J.-P. Vert. LOTUS: a single- and multitask machine learning algorithm for the prediction of cancer driver genes. PLoS Comp. Bio., 15(9):e1007381, 2019.
  35. J. Liu, Y. Huang, R. Singh, J.-P. Vert, & W. S. Noble. Jointly embedding multiple single-cell omics measurements. In K.T. Huber, & D. Gusfield (Eds), 19th International Workshop on Algorithms in Bioinformatics (WABI 2019), volume 143 of LIPIcs, 10:1–10:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.
  36. A. G. Cauer, G. Yardimci, J.-P. Vert, N. Varoquaux, & W. S. Noble. Inferring diploid 3d chromatin structures from hi-c data. In K.T. Huber, & D. Gusfield (Eds), 19th International Workshop on Algorithms in Bioinformatics (WABI 2019), volume 143 of LIPIcs, 11:1–11:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.
  37. B. Khalfaoui, J. Boyd, & J.-P. Vert. Adaptive structured noise injection for shallow and deep neural networks. Technical Report arXiv 1909.09819, 2019.
  38. L. Slim, C. Chatelain, C.-A. Azencott, & J.-P. Vert. kernelPSI: a post-selection inference framework for nonlinear variable selection. In K. Chaudhuri, & R. Salakhutdinov (Eds), Proceedings of the 36th International Conference on Machine Learning, (ICML 2019), volume 97 of Proceedings of Machine Learning Research, 5857–5865. PMLR, 2019.
  39. R. Menegaux, & J.-P. Vert. Continuous embeddings of DNA sequencing reads and application to metagenomics. J. Comput. Biol., 26(6):509–518, 2019.
  40. G. Dulac-Arnold, N. Zeghidour, M. Cuturi, L. Beyer, & J.-P. Vert. Deep multi-class learning from label proportions. Technical Report arXiv 1905.12909, 2019.
  41. E. Pauwels, F. R. Bach, & J.-P. Vert. Relating leverage scores and density using regularized Christoffel functions. In S. Bengio, H.M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds), Advances in Neural Information Processing Systems (NeurIPS 2018), volume 31, 1670–1679. Curran Associates, Inc., 2018.
  42. N. Servant, N. Varoquaux, E. Heard, E. Barillot, & J.-P. Vert. Effective normalization for copy number variation in Hi-C data. BMC Bioinform., 19:313, 2018.
  43. M. Le Morvan, & J.-P. Vert. WHInter: a working set algorithm for high-dimensional sparse second order interaction models. In J.G. Dy, & A. Krause (Eds), Proceedings of the 35th International Conference on Machine Learning, (ICML 2018), volume 80 of Proceedings of Machine Learning Research, 3632–3641. PMLR, 2018.
  44. Y. Jiao, & J.-P. Vert. The weighted Kendall and high-order kernels for permutations. In J.G. Dy, & A. Krause (Eds), Proceedings of the 35th International Conference on Machine Learning (ICML 2018), volume 80 of Proceedings of Machine Learning Research, 2319–2327. PMLR, 2018.
  45. Y. Jiao, & J.-P. Vert. The Kendall and Mallows kernels for permutations. IEEE Trans. Pattern Anal. Mach. Intell., 40(7):1755–1769, 2018.
  46. K. Vervier, P. Mahé, & J.-P. Vert. MetaVW: large-scale machine learning for metagenomics sequence classification. In H. Mamitsuka (Ed), Data Mining for Systems Biology: Methods and Protocols, pages 9–20. Springer New York, 2018.
  47. A. Recanati, N. Servant, J.-P. Vert, & A. d'Aspremont. Robust seriation and applications to cancer genomics. Technical Report arXiv 1806.00664, 2018.
  48. E. M. Bunnik, K. B. Cook, N. Varoquaux, G. Batugedara, J. Prudhomme, A. Cort, … K. G. Le Roch. Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages. Nature Comm., 9:1910, 2018.
  49. K. Van den Berge, F. Perraudeau, C. Soneson, M. I. Love, D. Risso, J.-P. Vert, … L. Clement. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol., 19:24, 2018.
  50. P. Ruan, M. Hayashida, T. Akutsu, & J.-P. Vert. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel. BMC Bioinform., 19:39, 2018.
  51. B. Khalfaoui, & J.-P. Vert. DropLasso: a robust variant of lasso for single cell RNA-seq data. Technical Report arXiv 1802.09381, 2018.
  52. D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit, & J.-P. Vert. A general and flexible method for signal extraction from single-cell RNA-seq data. Nature Comm., 9:284, 2018.
  53. J.-P. Vert. Quand les algorithmes font parler l'ADN. La Recherche, 529:48–52, 2017.
  54. E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter, & J.-P. Vert. Kernel multitask regression for toxicogenetics. Mol. Inform., 36(10):1700053, 2017.
  55. J.-L. Plouhinec, S. Medina-Ruiz, C. Borday, E. Bernard, J.-P. Vert, M. B. Eisen, … A. H. Monsoro-Burq. A molecular atlas of the developing ectoderm defines neural, neural crest, placode, and nonneural progenitor identity in vertebrates. PLoS Biol., 15(10):e2004045, 2017.
  56. M. Le Morvan, & J.-P. Vert. Supervised quantile normalisation. Technical Report arXiv 1706.00244, 2017.
  57. Y. Jiao, M. R. Hidalgo, C. Çubuk, A. Amadoz, J. Carbonell-Caballero, J.-P. Vert, & J. Dopazo. Signaling pathway activities improve prognosis for breast cancer. Technical Report bioRxiv 132357, 2017.
  58. M. Le Morvan, A. Zinovyev, & J.-P. Vert. NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis. PLoS Comp. Bio., 13(6):e1005573, 2017.
  59. S. K. Sieberts, F. Zhu, J. García-García, E. Stahl, A. Pratap, G. Pandey, … L. M. Mangravite. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Comm., 7:12460, 2016.
  60. K. Vervier, P. Mahé, M. Tournoud, J.-B. Veyrieras, & J.-P. Vert. Large-scale machine learning for metagenomics sequence classification. Bioinformatics, 32(7):1023–1032, 2016.
  61. J.-P. Vert. Le big data dans la recherche médicale. Revue des Ingénieurs des Mines, 488:8–9, 2016.
  62. N. Servant, N. Varoquaux, B. R. Lajoie, E. Viara, C.-J. Chen, J.-P. Vert, … E. Barillot. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol., 16:259, 2015.
  63. M. Moarii, F. Reyal, & J.-P. Vert. Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype. Hum. Genomics, 9:26, 2015.
  64. M. Moarii, V. Boeva, J.-P. Vert, & F. Reyal. Changes in correlation between promoter methylation and gene expression in cancer. BMC Genom., 16:873, 2015.
  65. L. Guyon, C. Lajaunie, F. Fer, R. Bhajun, E. Sulpice, G. Pinna, … X. Gidrol. Φ-score: a cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays. Sci. Rep., 5:14221, 2015.
  66. F. Eduati, L. M. Mangravite, T. Wang, H. Tang, J. C. Bare, R. Huang, … J. Saez-Rodriguez. Prediction of human population responses to toxic compounds by a collaborative competition. Nat. Biotechnol., 33(9):933–940, 2015.
  67. R. Fouladi, C. Schurmann, K. Bessonov, J.-P. Vert, R. J. F. Loos, & K. Van Steen. A novel gene-based analysis method based on MB-MDR. Genet. Epidemiol., 39(7):548–548, 2015.
  68. E. Bernard, L. Jacob, J. Mairal, E. Viara, & J.-P. Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. BMC Bioinform., 16:262, 2015.
  69. K. Vervier, P. Mahé, J.-B. Veyrieras, & J.-P. Vert. Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data. Technical Report arXiv 1506.07251, 2015.
  70. N. Varoquaux, I. Liachko, F. Ay, J. N. Burton, J. Shendure, M. J. Dunham, … W. S. Noble. Accurate identification of centromere locations in yeast genomes using Hi-C. Nucleic Acids Res., 43(11):5331–5339, 2015.
  71. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, & T. Walter. A generic methodological framework for studying single cell motility in high-throughput time-lapse data. Bioinformatics, 31:i320–i328, 2015.
  72. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, & T. Walter. Infering an ontology of single cell motions from high-throughput microscopy data. In Proc. IEEE 12th Int. Symp. Biomedical Imaging (ISBI 2015), 160–163. 2015.
  73. F. Ay, E. M. Bunnik, N. Varoquaux, J.-P. Vert, W. S. Noble, & K. G. Le Roch. Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum. BioEssays, 37(2):182–194, 2015.
  74. F. Ay, T. H. Vu, M. J. Zeitz, N. Varoquaux, J. E. Carette, J.-P. Vert, … W. S. Noble. Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C. BMC Genom., 16:121, 2015.
  75. Y. Jiao, & J.-P. Vert. The Kendall and Mallows kernels for permutations. In F. R. Bach, & D. M. Blei (Eds), Proceedings of the 32nd International Conference on Machine Learning, (ICML 2015), volume 37 of JMLR Workshop and Conference Proceedings, 1935–1944. JMLR.org, 2015.
  76. E. Scornet, G. Biau, & J.-P. Vert. Consistency of random forests. Ann. Stat., 43(4):1716–1741, 2015.
  77. E. Richard, G. Obozinski, & J.-P. Vert. Tight convex relaxations for sparse matrix factorization. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds), Advances in Neural Information Processing Systems (NIPS 2014), volume 27, 3284–3292. Curran Associates, Inc., 2014.
  78. J. C. Costello, L. M. Heiser, E. Georgii, M. Gönen, M. P. Menden, N. J. Wang, … G. Stolovitzky. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol., 32(12):1202–1212, 2014.
  79. K. Vervier, P. Mahé, A. D’Aspremont, J.-B. Veyrieras, & J.-P. Vert. On learning matrices with orthogonal columns or disjoint supports. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds), Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), volume 8726 of Lecture Notes in Computer Science, pages 274–289. Springer Berlin Heidelberg, 2014.
  80. E. Bernard, L. Jacob, J. Mairal, & J.-P. Vert. Efficient RNA isoform identification and quantification from RNA-seq data with network flows. Bioinformatics, 30(17):2447–2455, 2014.
  81. C. Tourette, F. Farina, R. P. Vazquez-Manrique, A.-M. Orfila, J. Voisin, S. Hernandez, … C. Neri. B19 the Wnt receptor Ryk reduces neuronal resistance capacity by repressing FOXO activity during the early phases of huntingtin pathogenicity. J. Neurol. Neurosurg. Psychiatry, 85(Suppl 1):A15–A15, 2014.
  82. E. Pauwels, C. Lajaunie, & J.-P. Vert. A Bayesian active learning strategy for sequential experimental design in systems biology. BMC Syst. Biol., 8(1):102, 2014.
  83. M. Moarii, A. Pinheiro, B. Sigal-Zafrani, A. Fourquet, M. Caly, N. Servant, … F. Reyal. Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences. PLoS One, 9:e103986, 2014.
  84. F. Ay, E. M. Bunnik, N. Varoquaux, S. M. Bol, J. Prudhomme, J.-P. Vert, … 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 Res., 24(6):974–988, 2014.
  85. C. Tourette, F. Farina, R. P. Vazquez-Manrique, A.-M. Orfila, J. Voisin, S. Hernandez, … C. Neri. The Wnt receptor Ryk reduces neuronal and cell survival capacity by repressing FOXO activity during the early phases of mutant huntingtin pathogenicity. PLoS Biol., 12:e1001895, 2014.
  86. T. D. Hocking, V. Boeva, G. Rigaill, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre, … J.-P. Vert. SegAnnDB: interactive Web-based genomic segmentation. Bioinformatics, 30(11):1539–1546, 2014.
  87. N. Varoquaux, F. Ay, W. S. Noble, & J.-P. Vert. A statistical approach for inferring the 3D structure of the genome. Bioinformatics, 30(12):i26–i33, 2014.
  88. F. Coste, C. Nédellec, T. Schiex, & J.-P. Vert. Bioinformatique. In P. Marquis, O. Papini, & H. Prade (Eds), Panorama de l'intelligence artificielle: Ses bases méthodologiques, ses développements, volume 3, pages 987–1008. Cépaduès, 2014.
  89. J.-L. Plouhinec, D. D. Roche, C. Pegoraro, A. L. Figueiredo, F. Maczkowiak, L. J. Brunet, … 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. Dev. Biol., 386(2):461–472, 2014.
  90. F. Mordelet, & J.-P. Vert. A bagging SVM to learn from positive and unlabeled examples. Pattern Recognition Lett., 37:201–209, 2014.
  91. Y. Zhao, T. Tamura, T. Akutsu, & 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.
  92. T. D. Hocking, G. Rigaill, J.-P. Vert, & F. R. Bach. Learning sparse penalties for change-point detection using max margin interval regression. In S. Dasgupta, & D. McAllester (Eds), Proceedings of the 30th International Conference on Machine Learning, (ICML 2013), volume 28 of Proceedings of Machine Learning Research, 172–180. PMLR, 2013.
  93. E. Richard, F. R. Bach, & J.-P. Vert. Intersecting singularities for multi-structured estimation. In S. Dasgupta, & D. McAllester (Eds), Proceedings of the 30th International Conference on Machine Learning (ICML 2013), volume 28 of Proceedings of Machine Learning Research, 1157–1165. PMLR, 2013.
  94. T. D. Hocking, G. Schleiermacher, I. Janoueix-Lerosey, V. Boeva, J. Cappo, O. Delattre, … J.-P. Vert. Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinform., 14:164, 2013.
  95. J.-P. Vert. Les applications industrielles de la bioinformatique. Annales des Mines - Réalités industrielles, pages 17–23, 2013.
  96. F. Mordelet, & J.-P. Vert. Supervised inference of gene regulatory networks from positive and unlabeled examples. In H. Mamitsuka, C. DeLisi, & M. Kanehisa (Eds), Data Mining for Systems Biology: Methods and Protocols, volume 939, pages 47–58. Humana Press, 2013.
  97. A.-C. Haury, F. Mordelet, P. Vera-Licona, & J.-P. Vert. TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst. Biol., 6:145, 2012.
  98. O. Filhol, D. Ciais, C. Lajaunie, P. Charbonnier, N. Foveau, J.-P. Vert, & Y. Vandenbrouck. DSIR: assessing the design of highly potent sirna by testing a set of cancer-relevant target genes. PLoS One, 7:e48057, 2012.
  99. E. Barillot, L. Calzone, P. Hupé, J.-P. Vert, & A. Zinovyev. Computational Systems Biology of Cancer. CRC Press, 2012.
  100. C. Houdayer, V. Caux-Moncoutier, S. Krieger, M. Barrois, F. Bonnet, V. Bourdon, … 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. Hum. Mutat., 33:1228–1238, 2012.
  101. D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill, D. M. Camacho, … G. Stolovitzky. Wisdom of crowds for robust gene network inference. Nat. Methods, 9(8):796–804, 2012.
  102. F.-X. Lejeune, L. Mesrob, F. Parmentier, C. Bicep, R. P. Vazquez-Manrique, J. A. Parker, … C. Neri. Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons. BMC Genom., 13:91, 2012.
  103. K. Takemoto, T. Tamura, Y. Cong, W.-K. Ching, J.-P. Vert, & T. Akutsu. Analysis of the impact degree distribution inmetabolic networks using branching process approximation. Physica A, 391(1–2):379–387, 2012.
  104. A.-C. Haury, P. Gestraud, & J.-P. Vert. The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PLoS One, 6(12):e28210, 2011.
  105. F. Mordelet, & J.-P. Vert. ProDiGe: prioritization of disease genes with multitask machine learning from positive and unlabeled examples. BMC Bioinf., 12:389, 2011.
  106. K. Bleakley, & J.-P. Vert. The group fused lasso for multiple change-point detection. Technical Report arXiv 1106.4199, 2011.
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  109. T. Matsui, M. Goto, J.-P. Vert, & Y. Uchiyama. Gradient-based musical feature extraction based on scale-invariant feature transform. In Proceedings of the 19th European Signal Processing Conference (EUSIPCO 2011), 724–728. IEEE, 2011.
  110. V. Boeva, A. Zinovyev, K. Bleakley, J.-P. Vert, I. Janoueix-Lerosey, O. Delattre, & E. Barillot. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics, 27(2):268–269, 2011.
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  113. L. Mesrob, F.-X. Lejeune, C. Bicep, J.-P. Vert, & C. Néri. Towards the unbiased prioritisation of huntington's disease targets using network based analysis of genome wide datasets. J. Neurol. Neurosurg. Psychiatry, 81(Suppl 1):A12–A12, 2010.
  114. M. Zaslavskiy, F. R. Bach, & J.-P. Vert. Many-to-many graph matching: a continuous relaxation approach. In J. Balcázar, F. Bonchi, A. Gionis, & M. Sebag (Eds), Machine Learning and Knowledge Discovery in Databases, volume 6323 of Lecture Notes in Computer Science, 515–530. Springer Berlin / Heidelberg, 2010.
  115. M. Hue, & J.-P. Vert. On learning with kernels for unordered pairs. In J. Fürnkranz, & T. Joachims (Eds), Proceedings of the 27th International Conference on Machine Learning (ICML 10), 463–470. Omnipress, 2010.
  116. M. Hue, M. Riffle, J.-P. Vert, & W. S. Noble. Large-scale prediction of protein-protein interactions from structures. BMC Bioinf., 11:144, 2010.
  117. B. Hoffmann, M. Zaslavskiy, J.-P. Vert, & V. Stoven. A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3d: application to ligand prediction. BMC Bioinf., 11:99, 2010.
  118. J.-P. Vert. Reconstruction of biological networks by supervised machine learning approaches. In H. M. Lohdi, & S. H. Muggleton (Eds), Elements of Computational Systems Biology, pages 189–212. Wiley, 2010.
  119. A.-C. Haury, L. Jacob, & J.-P. Vert. Increasing stability and interpretability of gene expression signatures. Technical Report arXiv 1001.3109, 2010.
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  121. M. Zaslavskiy, F. R. Bach, & J.-P. Vert. A path following algorithm for the graph matching problem. IEEE Trans. Pattern Anal. Mach. Intell., 31(12):2227–2242, 2009.
  122. K. Bleakley, & J.-P. Vert. Joint segmentation of many aCGH profiles using fast group LARS. Technical Report arXiv 0910.1167, 2009.
  123. P. Mahé, & J.-P. Vert. Virtual screening with support vector machines and structure kernels. Comb. Chem. High Throughput Screen., 12(4):409–423, 2009.
  124. J.-P. Vert, T. Matsui, Shin'ichi Satoh, & Y. Uchiyama. High-level feature extraction using SVM with walk-based graph kernel. In Proceedings of the IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2009), 1121–1124. 2009.
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  126. P. Mahé, & J.-P. Vert. Graph kernels based on tree patterns for molecules. Mach. Learn., 75(1):3–35, 2009.
  127. M. Zaslavskiy, F. R. Bach, & J.-P. Vert. Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics, 25(12):i259–i267, 2009.
  128. J. Abernethy, F. R. Bach, T. Evgeniou, & J.-P. Vert. A new approach to collaborative filtering: operator estimation with spectral regularization. J. Mach. Learn. Res., 10:803–826, 2009.
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  130. L. Jacob, & J.-P. Vert. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinform., 24(19):2149–2156, 2008.
  131. L. Jacob, B. Hoffmann, V. Stoven, & J.-P. Vert. Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinf., 9:363, 2008.
  132. J.-P. Vert, & L. Jacob. Machine learning for in silico virtual screening and chemical genomics: new strategies. Comb. Chem. High Throughput Screening, 11(8):677–685, 2008.
  133. F. Mordelet, & J.-P. Vert. SIRENE: supervised inference of regulatory networks. Bioinformatics, 24(16):i76–i82, 2008.
  134. F. Rapaport, E. Barillot, & J.-P. Vert. Classification of arrayCGH data using fused SVM. Bioinformatics, 24(13):i375–i382, 2008.
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  136. J. Abernethy, T. Evgeniou, O. Toubia, & J.-P. Vert. Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans. Knowl. Data Eng., 20(2):145–155, 2008.
  137. L. Jacob, & J.-P. Vert. Efficient peptide-MHC-i binding prediction for alleles with few known binders. Bioinformatics, 24(3):358–366, 2008.
  138. J.-P. Vert. The optimal assignment kernel is not positive definite. Technical Report arXiv 0801.4061, 2008.
  139. J.-P. Vert, J. Qiu, & W. S. Noble. A new pairwise kernel for biological network inference with support vector machines. BMC Bioinf., 8 Suppl 10:S8, 2007.
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  141. Y. Yamanishi, F. R. Bach, & J.-P. Vert. Glycan classification with tree kernels. Bioinformatics, 23(10):1211–1216, 2007.
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  145. F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, & J.-P. Vert. Classification of microarray data using gene networks. BMC Bioinf., 8:35, 2007.
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  147. J.-P. Vert. Kernel methods in genomics and computational biology. In G. Camps-Valls, J.-L. Rojo-Alvarez, & M. Martinez-Ramon (Eds), Kernel Methods in Bioengineering, Signal and Image Processing, pages 42–63. IDEA Group, 2006.
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  151. F. Rapaport, A. Zinovyev, E. Barillot, & J.-P. Vert. Spectral analysis of gene expression profiles using gene networks. In Proceedings of the Fifth International Conference on Bioinformatics of Genome Regulation and Structure (BGRS'2006), volume 3, 91–95. 2006.
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  153. R. Vert, & J.-P. Vert. Consistency and convergence rates of one-class SVMs and related algorithms. J. Mach. Learn. Res., 7(29):817–854, 2006.
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  157. S. Matsuda, J.-P. Vert, H. Saigo, N. Ueda, H. Toh, & T. Akutsu. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci., 14(11):2804–2813, 2005.
  158. M. Cuturi, & J.-P. Vert. The context-tree kernel for strings. Neural Netw., 18(8):1111–1123, 2005.
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  164. J.-P. Vert. Kernel methods in computational biology. 2004. Habilitation à diriger les recherches (HDR), Université Paris 6.
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  166. J.-P. Vert, H. Saigo, & T. Akutsu. Local alignment kernels for biological sequences. In B. Schölkopf, K. Tsuda, & J.-P. Vert (Eds), Kernel Methods in Computational Biology, pages 131–154. MIT Press, 2004.
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  169. P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret, & J.-P. Vert. Extensions of marginalized graph kernels. In R. Greiner, & D. Schuurmans (Eds), Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 552–559. New York, NY, USA, 2004. ACM Press.
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  172. M. Cuturi, & J.-P. Vert. A mutual information kernel for sequences. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2004), volume 3, 1905–1910. 2004.
  173. H. Saigo, J.-P. Vert, N. Ueda, & T. Akutsu. Protein homology detection using string alignment kernels. Bioinformatics, 20(11):1682–1689, 2004.
  174. J.-P. Vert. Kernel methods in computational biology. 物性研究, 81(1):142–155, 2003.
  175. J.-P. Vert. An introduction to DNA microarrays and some mathematical challenges behind them. 物性研究, 81(1):130–141, 2003.
  176. J.-P. Vert, & M. Kanehisa. Extracting active pathways from gene expression data. Bioinformatics, 19(suppl 2):ii238–ii234, 2003.
  177. Y. Yamanishi, J.-P. Vert, A. Nakaya, & M. Kanehisa. Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. Bioinformatics, 19(Suppl. 1):i323–i330, 2003.
  178. H. Saigo, J.-P. Vert, T. Akutsu, & N. Ueda. Comparison of SVM-based methods for remote homology detection. Genome Inform., 13:396–397, 2002.
  179. J.-P. Vert. A tree kernel to analyze phylogenetic profiles. Bioinformatics, 18(suppl 1):S276–S284, 2002.
  180. K. Nakai, & J.-P. Vert. Genome informatics for data-driven biology. Genome Biol., 3:reports4010.1, 2002.
  181. J.-P. Vert. Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. In R. B. Altman, A. K. Dunker, L. Hunter, K. Lauerdale, & T. E. Klein (Eds), Pac Symp Biocomput. 2002, 649–660. World Scientific, 2002.
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  183. Y. Igarashi, Y. Okuno, J.-P. Vert, & M. Kanehisa. Detecting transcriptional cis-regulation from gene expression data. Genome Inform., 12:241–242, 2001.
  184. J.-P. Vert. Adaptive context trees and text clustering. IEEE Trans. Inform. Theory, 47(5):1884–1901, 2001.
  185. J.-P. Vert. Text categorization using adaptive context trees. In A. Gelbukh (Ed), Computational Linguistics and Intelligent Text Processing (CICLing 2001), volume 2004 of LNCS, 423–436. Berlin, Heidelberg, 2001. Springer Verlag.
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  189. J.-P. Vert. Overview or research in written language processing in japan. 1998. Corps des Mines master thesis, Ecole des Mines de Paris.