What is he best known for?
The fields of study he is best known for:
- Statistics
- Gene
- Artificial intelligence
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.
His most cited work include:
- Group lasso with overlap and graph lasso (741 citations)
- Kernel Methods in Computational Biology (733 citations)
- HiC-Pro: an optimized and flexible pipeline for Hi-C data processing (686 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (56.71%)
- Machine learning (42.95%)
- Computational biology (34.90%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (34.90%)
- Genomics (8.39%)
- Inference (21.48%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Computational Systems Biology of Cancer (65 citations)
- Learning with Differentiable Perturbed Optimizers. (17 citations)
- Transcriptional Programs Define Intratumoral Heterogeneity of Ewing Sarcoma at Single-Cell Resolution. (17 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Gene
- Artificial intelligence
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.
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