His primary areas of study are Computational biology, Artificial intelligence, Support vector machine, Machine learning and Kernel method. His Computational biology research incorporates elements of Genetics, Data mining, Small interfering RNA, DNA microarray and Gene regulatory network. His research on Genetics often connects related topics like Drug target.
His work deals with themes such as Stability, Regulation of gene expression, Selection and Systems biology, which intersect with Data mining. His Artificial intelligence research includes elements of Bioinformatics and Pattern recognition. His work in the fields of Machine learning, such as Supervised learning, overlaps with other areas such as Context.
Jean-Philippe Vert mainly investigates Artificial intelligence, Machine learning, Computational biology, Support vector machine and Genetics. In the subject of general Artificial intelligence, his work in Kernel, Kernel method and Inference is often linked to Context, thereby combining diverse domains of study. His Machine learning research incorporates themes from Metagenomics, DNA sequencing and Identification.
His Computational biology study integrates concerns from other disciplines, such as Data mining, Genomics, DNA microarray, Small molecule and Drug discovery. The study incorporates disciplines such as Virtual screening and Theoretical computer science in addition to Support vector machine. The Genetics study which covers Neuroscience that intersects with Neural plate and Neural crest.
His scientific interests lie mostly in Computational biology, Genomics, Inference, Algorithm and Differentiable function. His biological study spans a wide range of topics, including Cancer, Genome and Statistical model. He has included themes like Normalization, Artificial intelligence and Flexibility in his Genomics study.
Artificial intelligence is closely attributed to Gold standard in his study. His Inference research includes themes of Data mining, Identification, Cluster analysis, Dimensionality reduction and Principal component analysis. His Cell research includes elements of Regulation of gene expression and Gene regulatory network.
His primary areas of study are Chromatin, RNA, Cell, Computational biology and Algorithm. His Chromatin study combines topics in areas such as Read depth, Transcriptome, Cell growth and Sarcoma. His work deals with themes such as Inference, Cell biology, Regulation of gene expression, Cell type and Gene regulatory network, which intersect with RNA.
His Computational biology research includes themes of Matrix, Replicate, Chromatin conformation, Chromosome and Statistical model. Jean-Philippe Vert has included themes like RNA-Seq and Cellular differentiation in his Algorithm study.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing
Nicolas Servant;Nelle Varoquaux;Nelle Varoquaux;Nelle Varoquaux;Bryan R. Lajoie;Eric Viara.
Genome Biology (2015)
Group lasso with overlap and graph lasso
Laurent Jacob;Guillaume Obozinski;Jean-Philippe Vert.
international conference on machine learning (2009)
Kernel Methods in Computational Biology
Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert.
Protein homology detection using string alignment kernels
Hiroto Saigo;Jean-Philippe Vert;Nobuhisa Ueda;Tatsuya Akutsu.
Clustered Multi-Task Learning: A Convex Formulation
Laurent Jacob;Jean-philippe Vert;Francis R. Bach.
neural information processing systems (2008)
A Primer on Kernel Methods
JP Vert;K Tsuda;B Schölkopf;B. Schölkopf K. Tsuda.
Protein-ligand interaction prediction
Laurent Jacob;Jean-Philippe Vert.
A general and flexible method for signal extraction from single-cell RNA-seq data
Davide Risso;Fanny Perraudeau;Svetlana Gribkova;Sandrine Dudoit.
Nature Communications (2018)
A Path Following Algorithm for the Graph Matching Problem
M. Zaslavskiy;F. Bach;J.-P. Vert.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
Anne-Claire Haury;Pierre Gestraud;Jean-Philippe Vert.
PLOS ONE (2011)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: