Jean-Philippe Vert

Jean-Philippe Vert

Professor, Chief Research and Development Officer at Owkin

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Pages

Posts

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Blog Post number 1

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portfolio

publications

Overview or research in written language processing in Japan

J.-P. Vert. Overview or research in written language processing in japan. 1998. Corps des Mines master thesis, Ecole des Mines de Paris.

Le consensuisse

M. Chevrel, & J.-P. Vert. Le consensuisse. Annales des Mines - Réalités industrielles, pages 41–46, 1999.

Double mixture and universal inference

J.-P. Vert. Double mixture and universal inference. Technical Report Ecole Normale Supérieure DMA-00-15, 2000.

Statistical Methods for Natural Language Modelling

J.-P. Vert. Statistical Methods for Natural Language Modelling. PhD thesis, Paris 6 University, 2001.

Text categorization using adaptive context trees

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.

Adaptive context trees and text clustering

J.-P. Vert. Adaptive context trees and text clustering. IEEE Trans. Inform. Theory, 47(5):1884–1901, 2001.

Detecting Transcriptional cis-Regulation from Gene Expression Data

Y. Igarashi, Y. Okuno, J.-P. Vert, & M. Kanehisa. Detecting transcriptional cis-regulation from gene expression data. Genome Inform., 12:241–242, 2001.

Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA

J.-P. Vert, & M. Kanehisa. Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA. In S. Becker, S. Thrun, & K. Obermayer (Eds), Advances in Neural Information Processing Systems (NIPS 2002), volume 15, 1449–1456. MIT Press, 2002.

Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings

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.

Genome informatics for data-driven biology

K. Nakai, & J.-P. Vert. Genome informatics for data-driven biology. Genome Biol., 3:reports4010.1, 2002.

A tree kernel to analyze phylogenetic profiles

J.-P. Vert. A tree kernel to analyze phylogenetic profiles. Bioinformatics, 18(suppl 1):S276–S284, 2002.

Comparison of SVM-Based Methods for Remote Homology Detection

H. Saigo, J.-P. Vert, T. Akutsu, & N. Ueda. Comparison of SVM-based methods for remote homology detection. Genome Inform., 13:396–397, 2002.

Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis

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.

Extracting active pathways from gene expression data

J.-P. Vert, & M. Kanehisa. Extracting active pathways from gene expression data. Bioinformatics, 19(suppl 2):ii238–ii234, 2003.

An introduction to DNA microarrays and some mathematical challenges behind them

J.-P. Vert. An introduction to DNA microarrays and some mathematical challenges behind them. 物性研究, 81(1):130–141, 2003.

Kernel methods in computational biology

J.-P. Vert. Kernel methods in computational biology. 物性研究, 81(1):142–155, 2003.

Protein homology detection using string alignment kernels

H. Saigo, J.-P. Vert, N. Ueda, & T. Akutsu. Protein homology detection using string alignment kernels. Bioinformatics, 20(11):1682–1689, 2004.

A mutual information kernel for sequences

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.

A primer on kernel methods

J.-P. Vert, K. Tsuda, & B. Schölkopf. A primer on kernel methods. In B. Schölkopf, K. Tsuda, & J.-P. Vert (Eds), Kernel Methods in Computational Biology, pages 35–70. MIT Press, 2004.

Diffusion kernels

R. Kondor, & J.-P. Vert. Diffusion kernels. In B. Schölkopf, K. Tsuda, & J.-P. Vert (Eds), Kernel Methods in Computational Biology, pages 171–192. MIT Press, 2004.

Extensions of marginalized graph kernels

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.

Heterogeneous data comparison and gene selection with kernel canonical correlation analysis

Y. Yamanishi, J.-P. Vert, & M. Kanehisa. Heterogeneous data comparison and gene selection with kernel canonical correlation analysis. In B. Schölkopf, K. Tsuda, & J.-P. Vert (Eds), Kernel Methods in Computational Biology, pages 209–230. MIT Press, 2004.

Kernel Methods in Computational Biology

B. Schölkopf, K. Tsuda, & J.-P. Vert. Kernel Methods in Computational Biology. MIT Press, 2004.

Local alignment kernels for biological sequences

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.

Protein network inference from multiple genomic data: a supervised approach

Y. Yamanishi, J.-P. Vert, & M. Kanehisa. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics, 20(suppl 1):i363–i370, 2004.

Kernel methods in computational biology

J.-P. Vert. Kernel methods in computational biology. 2004. Habilitation à diriger les recherches (HDR), Université Paris 6.

Semigroup Kernels on Finite Sets

M. Cuturi, & J.-P. Vert. Semigroup kernels on finite sets. In L. K. Saul, Y. Weiss, & L. Bottou (Eds), Advances in Neural Information Processing Systems (NIPS 2004), volume 17, 329–336. Cambridge, MA, 2004. MIT Press.

Supervised graph inference

J.-P. Vert, & Y. Yamanishi. Supervised graph inference. In L. K. Saul, Y. Weiss, & L. Bottou (Eds), Advances in Neural Information Processing Systems (NIPS 2004), volume 17, 1433–1440. Cambridge, MA, 2004. MIT Press.

Graph kernels for molecular structure-activity relationship analysis with support vector machines

P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret, & J.-P. Vert. Graph kernels for molecular structure-activity relationship analysis with support vector machines. J. Chem. Inf. Model., 45(4):939–51, 2005.

Supervised enzyme network inference from the integration of genomic data and chemical information

Y. Yamanishi, J.-P. Vert, & M. Kanehisa. Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics, 21(suppl 1):i468–i477, 2005.

Semigroup Kernels on Measures

M. Cuturi, K. Fukumizu, & J.-P. Vert. Semigroup kernels on measures. J. Mach. Learn. Res., 6:1169–1198, 2005.

The context-tree kernel for strings

M. Cuturi, & J.-P. Vert. The context-tree kernel for strings. Neural Netw., 18(8):1111–1123, 2005.

A novel representation of protein sequences for prediction of subcellular location using support vector machines.

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.

Consistency of one-class SVM and related algorithms

R. Vert, & J.-P. Vert. Consistency of one-class SVM and related algorithms. In Y. Weiss, B. Schölkopf, & J. Platt (Eds), Advances in Neural Information Processing Systems (NIPS 2005), volume 18, 1409–1416. Cambridge, MA, 2005. MIT Press.

Kernels for gene regulatory regions

J.-P. Vert, R. Thurman, & W. S. Noble. Kernels for gene regulatory regions. In Y. Weiss, B. Schölkopf, & J. Platt (Eds), Advances in Neural Information Processing Systems (NIPS 2005), volume 18, 1401–1408. Cambridge, MA, 2005. MIT Press.

カーネル法による複数のゲノムデータからのタンパク質間機能ネットワークの推定

Y. Yamanishi, & J.-P. Vert. カーネル法による複数のゲノムデータからのタンパク質間機能ネットワークの推定. Proc. Inst. Statist. Math., 54(2):357–373, 2006.

Consistency and convergence rates of one-class SVMs and related algorithms

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.

Optimizing amino acid substitution matrices with a local alignment kernel.

H. Saigo, J.-P. Vert, & T. Akutsu. Optimizing amino acid substitution matrices with a local alignment kernel. BMC Bioinf., 7:246, 2006.

Spectral analysis of gene expression profiles using gene networks

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.

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

P. Mahé, L. Ralaivola, V. Stoven, & J.-P. Vert. The pharmacophore kernel for virtual screening with support vector machines. J. Chem. Inf. Model., 46(5):2003–2014, 2006.

Classification of Biological Sequences with Kernel Methods (ICGI 2006)

J.-P. Vert. Classification of biological sequences with kernel methods (icgi 2006). In Y. Sakakibara, S. Kobayashi, K. Sato, T. Nishino, & E. Tomita (Eds), Grammatical Inference: Algorithms and Applications, volume 4201 of Lecture Notes in Computer Science, 7–18. Springer Berlin Heidelberg, 2006.

An accurate and interpretable model for siRNA efficacy prediction

J.-P. Vert, N. Foveau, C. Lajaunie, & Y. Vandenbrouck. An accurate and interpretable model for siRNa efficacy prediction. BMC Bioinf., 7:520, 2006.

Kernel methods in genomics and computational biology

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.

Low-rank matrix factorization with attributes

J. Abernethy, F. R. Bach, T. Evgeniou, & J.-P. Vert. Low-rank matrix factorization with attributes. Technical Report arXiv cs/0611124, 2006.

Classification of microarray data using gene networks

F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, & J.-P. Vert. Classification of microarray data using gene networks. BMC Bioinf., 8:35, 2007.

Kernel matrix regression

Y. Yamanishi, & J.-P. Vert. Kernel matrix regression. Technical Report arXiv q-bio/0702054, 2007.

A Kernel for Time Series Based on Global Alignments

M. Cuturi, J.-P. Vert, Ø. Birkenes, & T. Matsui. A kernel for time series based on global alignments. In Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), volume 2, II–413–II–416. 2007.

A structural alignment kernel for protein structures.

J. Qiu, M. Hue, A. Ben-Hur, J.-P. Vert, & W. S. Noble. A structural alignment kernel for protein structures. Bioinformatics, 23(9):1090–1098, 2007.

Glycan classification with tree kernels

Y. Yamanishi, F. R. Bach, & J.-P. Vert. Glycan classification with tree kernels. Bioinformatics, 23(10):1211–1216, 2007.

Supervised reconstruction of biological networks with local models.

K. Bleakley, G. Biau, & J.-P. Vert. Supervised reconstruction of biological networks with local models. Bioinformatics, 23:i57–i65, 2007.

A new pairwise kernel for biological network inference with support vector machines

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.

The optimal assignment kernel is not positive definite

J.-P. Vert. The optimal assignment kernel is not positive definite. Technical Report arXiv 0801.4061, 2008.

Efficient peptide-MHC-I binding prediction for alleles with few known binders

L. Jacob, & J.-P. Vert. Efficient peptide-MHC-i binding prediction for alleles with few known binders. Bioinformatics, 24(3):358–366, 2008.

Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires

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.

A path following algorithm for graph matching

M. Zaslavskiy, F. R. Bach, & J.-P. Vert. A path following algorithm for graph matching. In A. Elmoataz, O. Lezoray, F. Nouboud, & D. Mammass (Eds), Proceedings of the 3rd International Conference on Image and Signal Processing (ICISP 2008), volume 5099 of LNCS, 329–337. Springer Berlin / Heidelberg, 2008.

Classification of arrayCGH data using fused SVM

F. Rapaport, E. Barillot, & J.-P. Vert. Classification of arrayCGH data using fused SVM. Bioinformatics, 24(13):i375–i382, 2008.

SIRENE: Supervised inference of regulatory networks

F. Mordelet, & J.-P. Vert. SIRENE: supervised inference of regulatory networks. Bioinformatics, 24(16):i76–i82, 2008.

Machine Learning for In Silico Virtual Screening and Chemical Genomics: New Strategies

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.

Virtual screening of GPCRs: an in silico chemogenomics approach.

L. Jacob, B. Hoffmann, V. Stoven, & J.-P. Vert. Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinf., 9:363, 2008.

Protein-ligand interaction prediction: an improved chemogenomics approach

L. Jacob, & J.-P. Vert. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinform., 24(19):2149–2156, 2008.

Clustered Multi-Task Learning: A Convex Formulation

L. Jacob, F. R. Bach, & J.-P. Vert. Clustered multi-task learning: a convex formulation. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds), Advances in Neural Information Processing Systems (NIPS 2008), volume 21, 745–752. Curran Associates, Inc., 2008.

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

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.

Global alignment of protein-protein interaction networks by graph matching methods

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.

Graph kernels based on tree patterns for molecules

P. Mahé, & J.-P. Vert. Graph kernels based on tree patterns for molecules. Mach. Learn., 75(1):3–35, 2009.

Group lasso with overlap and graph lasso

L. Jacob, G. Obozinski, & J.-P. Vert. Group lasso with overlap and graph lasso. In A. P. Danyluk, L. Bottou, & M. L. Littman (Eds), Proceedings of the 26th Annual International Conference on Machine Learning (ICML 2009), volume 382 of ACM International Conference Proceeding Series, 433–440. ACM, 2009.

High-level feature extraction using SVM with walk-based graph kernel

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.

Virtual screening with support vector machines and structure kernels.

P. Mahé, & J.-P. Vert. Virtual screening with support vector machines and structure kernels. Comb. Chem. High Throughput Screen., 12(4):409–423, 2009.

Joint segmentation of many aCGH profiles using fast group LARS

K. Bleakley, & J.-P. Vert. Joint segmentation of many aCGH profiles using fast group LARS. Technical Report arXiv 0910.1167, 2009.

A Path Following Algorithm for the Graph Matching Problem

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.

White Functionals for Anomaly Detection in Dynamical Systems

M. Cuturi, J.-P. Vert, & A. d'Aspremont. White functionals for anomaly detection in dynamical systems. In Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, & A. Culotta (Eds), Advances in Neural Information Processing Systems (NIPS 2009), volume 22, 432–440. Curran Associates, Inc., 2009.

Increasing stability and interpretability of gene expression signatures

A.-C. Haury, L. Jacob, & J.-P. Vert. Increasing stability and interpretability of gene expression signatures. Technical Report arXiv 1001.3109, 2010.

Reconstruction of biological networks by supervised machine learning approaches

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.

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction.

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.

Large-scale prediction of protein-protein interactions from structures.

M. Hue, M. Riffle, J.-P. Vert, & W. S. Noble. Large-scale prediction of protein-protein interactions from structures. BMC Bioinf., 11:144, 2010.

On learning with kernels for unordered pairs

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.

Many-to-Many Graph Matching: A Continuous Relaxation Approach

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.

Towards the unbiased prioritisation of Huntington's Disease targets using network based analysis of genome wide datasets

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.

Fast detection of multiple change-points shared by many signals using group LARS

J.-P. Vert, & K. Bleakley. 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, & A. Culotta (Eds), Advances in Neural Information Processing Systems (NIPS 2010), volume 23, 2343–2352. Curran Associates, Inc., 2010.

3D Ligand-Based Virtual Screening with Support Vector Machines

J.-P. Vert. 3d ligand-based virtual screening with support vector machines. In H. Lodhi, & Y. Yamanishi (Eds), Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, pages 35–45. IGI Global,, 2011.

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization.

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.

Gradient-based musical feature extraction based on scale-invariant feature transform

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.

Group Lasso with Overlaps: the Latent Group Lasso approach

G. Obozinski, L. Jacob, & J.-P. Vert. Group lasso with overlaps: the latent group lasso approach. Technical Report arXiv 1110.0413, 2011.

Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties

T. D. Hocking, J.-P. Vert, F. R. Bach, & A. Joulin. Clusterpath: an algorithm for clustering using convex fusion penalties. In L. Getoor, & T. Scheffer (Eds), Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 745–752. ACM, 2011.

The group fused Lasso for multiple change-point detection

K. Bleakley, & J.-P. Vert. The group fused lasso for multiple change-point detection. Technical Report arXiv 1106.4199, 2011.

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.

F. Mordelet, & J.-P. Vert. ProDiGe: prioritization of disease genes with multitask machine learning from positive and unlabeled examples. BMC Bioinf., 12:389, 2011.

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures.

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.

Analysis of the impact degree distribution inmetabolic networks using branching process approximation

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.

Large-scale functional RNAi screen in C. elegans identifies genes that regulate the dysfunction of mutant polyglutamine neurons

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.

Wisdom of crowds for robust gene network inference.

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.

Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants.

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.

Computational Systems Biology of Cancer

E. Barillot, L. Calzone, P. Hupé, J.-P. Vert, & A. Zinovyev. Computational Systems Biology of Cancer. CRC Press, 2012.

DSIR: assessing the design of highly potent siRNA by testing a set of cancer-relevant target genes.

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.

TIGRESS: Trustful Inference of Gene REgulation using Stability Selection.

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.

Supervised inference of gene regulatory networks from positive and unlabeled examples.

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.

Les applications industrielles de la bioinformatique

J.-P. Vert. Les applications industrielles de la bioinformatique. Annales des Mines - Réalités industrielles, pages 17–23, 2013.

Learning smoothing models of copy number profiles using breakpoint annotations

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.

Intersecting singularities for multi-structured estimation

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.

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression

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.

Flux balance impact degree: a new definition of impact degree to properly treat reversible reactions in metabolic networks.

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.

A bagging SVM to learn from positive and unlabeled examples

F. Mordelet, & J.-P. Vert. A bagging SVM to learn from positive and unlabeled examples. Pattern Recognition Lett., 37:201–209, 2014.

Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers.

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.

Bioinformatique

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.

A statistical approach for inferring the 3D structure of the genome.

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.

SegAnnDB: interactive Web-based genomic segmentation

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.

The Wnt receptor Ryk reduces neuronal and cell survival capacity by repressing FOXO activity during the early phases of mutant huntingtin pathogenicity.

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.

Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression.

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.

Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences.

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.

A Bayesian active learning strategy for sequential experimental design in systems biology.

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.

B19 The Wnt Receptor Ryk Reduces Neuronal Resistance Capacity By Repressing FOXO Activity During The Early Phases Of Huntingtin Pathogenicity

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.

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows.

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.

On Learning Matrices with Orthogonal Columns or Disjoint Supports

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.

A community effort to assess and improve drug sensitivity prediction algorithms.

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.

Tight convex relaxations for sparse matrix factorization

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.

Consistency of random forests

E. Scornet, G. Biau, & J.-P. Vert. Consistency of random forests. Ann. Stat., 43(4):1716–1741, 2015.

The Kendall and Mallows Kernels for Permutations

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.

Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C.

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.

Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum

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.

Infering an ontology of single cell motions from high-throughput microscopy data

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.

A generic methodological framework for studying single cell motility in high-throughput time-lapse data.

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.

Accurate identification of centromere locations in yeast genomes using Hi-C.

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.

Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data

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.

A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.

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.

A novel gene-based analysis method based on MB-MDR

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.

Prediction of human population responses to toxic compounds by a collaborative competition.

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.

Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays.

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.

Changes in correlation between promoter methylation and gene expression in cancer

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.

Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype

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.

HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

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.

Le Big Data dans la recherche médicale

J.-P. Vert. Le big data dans la recherche médicale. Revue des Ingénieurs des Mines, 488:8–9, 2016.

Large-scale machine learning for metagenomics sequence classification.

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.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

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.

NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

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.

Signaling Pathway Activities Improve Prognosis for Breast Cancer

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.

Supervised quantile normalisation

M. Le Morvan, & J.-P. Vert. Supervised quantile normalisation. Technical Report arXiv 1706.00244, 2017.

A molecular atlas of the developing ectoderm defines neural, neural crest, placode, and nonneural progenitor identity in vertebrates.

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.

Kernel Multitask Regression for Toxicogenetics

E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter, & J.-P. Vert. Kernel multitask regression for toxicogenetics. Mol. Inform., 36(10):1700053, 2017.

Quand les algorithmes font parler l'ADN

J.-P. Vert. Quand les algorithmes font parler l'ADN. La Recherche, 529:48–52, 2017.

A general and flexible method for signal extraction from single-cell RNA-seq data

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.

DropLasso: A robust variant of Lasso for single cell RNA-seq data

B. Khalfaoui, & J.-P. Vert. DropLasso: a robust variant of lasso for single cell RNA-seq data. Technical Report arXiv 1802.09381, 2018.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

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.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

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.

Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages.

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.

Robust Seriation and Applications to Cancer Genomics

A. Recanati, N. Servant, J.-P. Vert, & A. d'Aspremont. Robust seriation and applications to cancer genomics. Technical Report arXiv 1806.00664, 2018.

MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification

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.

The Kendall and Mallows Kernels for Permutations

Y. Jiao, & J.-P. Vert. The Kendall and Mallows kernels for permutations. IEEE Trans. Pattern Anal. Mach. Intell., 40(7):1755–1769, 2018.

The Weighted Kendall and High-order Kernels for Permutations

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.

WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models

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.

Effective normalization for copy number variation in Hi-C data.

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.

Relating Leverage Scores and Density using Regularized Christoffel Functions

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.

Deep multi-class learning from label proportions

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.

Continuous Embeddings of DNA Sequencing Reads and Application to Metagenomics

R. Menegaux, & J.-P. Vert. Continuous embeddings of DNA sequencing reads and application to metagenomics. J. Comput. Biol., 26(6):509–518, 2019.

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection

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.

Adaptive structured noise injection for shallow and deep neural networks

B. Khalfaoui, J. Boyd, & J.-P. Vert. Adaptive structured noise injection for shallow and deep neural networks. Technical Report arXiv 1909.09819, 2019.

Inferring Diploid 3D Chromatin Structures from Hi-C Data

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.

Jointly Embedding Multiple Single-Cell Omics Measurements

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.

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes.

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.

Differentiable Deep Clustering with Cluster Size Constraints

A. Genevay, G. Dulac-Arnold, & J.-P. Vert. Differentiable deep clustering with cluster size constraints. Technical Report arXiv 1910.09036, 2019.

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data.

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.

Differentiable Ranking and Sorting using Optimal Transport

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.

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models

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.

Transcriptional Programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution.

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.

Artificial Intelligence and Cancer Genomics

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.

Embedding the de Bruijn graph, and applications to metagenomics

R. Menegaux, & J.-P. Vert. Embedding the de Bruijn graph, and applications to metagenomics. Technical Report bioRxiv 2020.03.06.980979, 2020.

Measuring significant changes in chromatin conformation with ACCOST

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.

Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design

M. Cuturi, O. Teboul, & J.-P. Vert. Noisy adaptive group testing using bayesian sequential experimental design. Technical Report arXiv 2004.12508, 2020.

Supervised Quantile Normalization for Low Rank Matrix Factorization

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.

Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis.

F. Raimundo, C. Vallot, & J.-P. Vert. Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol., 21:212, 2020.

Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.

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.

Causal inference methods for combining randomized trials and observational studies: a review

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.

Novel methods for epistasis detection in genome-wide association studies.

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.

Learning with Differentiable Perturbed Optimizers

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.

AI-based mobile application to fight antibiotic resistance

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.

Differentiable divergences between time series

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.

Machine learning for single-cell genomics data analysis

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.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data.

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.

Framing RNN as a kernel method: A neural ODE approach

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.

Reverse-Complement Equivariant Networks for DNA Sequences

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.

Semi-supervised single-cell cross-modality translation using Polarbear

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.

Scaling ResNets in the large-depth regime

P. Marion, A. Fermanian, G. Biau, & J.-P. Vert. Scaling ResNets in the large-depth regime. Technical Report arXiv 2206.06929, 2022.

On Mixup Regularization

L. Carratino, M. Cissé, R. Jenatton, & J.-P. Vert. On mixup regularization. J. Mach. Learn. Res., 23(325):1–31, 2022.

Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning.

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.

Efficient and modular implicit differentiation

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.

A benchmark of computational pipelines for single-cell histone modification data

F. Raimundo, P. Prompsy, J.-P. Vert, & C. Vallot. A benchmark of computational pipelines for single-cell histone modification data. Genome Biol., 2023.

Deep embedding and alignment of protein sequences

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.

Inference of genome 3D architecture by modeling overdispersion of Hi-C data

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.

DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer

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.

Regression as Classification: Influence of Task Formulation on Neural Network Features

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.

How will generative AI disrupt data science in drug discovery?

J.-P. Vert. How will generative ai disrupt data science in drug discovery? Nat. Biotechnol., 2023.

LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis

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.

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