Jean Coquet

Office X251
Biomedical Informatics Research
1265 Welch Road
Stanford, California 94305

+1 (650) 723-1831

jean.coquet@stanford.edu

Jean Coquet

Postdoctoral Research Fellow in computer science

Department of Medicine, Center for Biomedical Informatics Research (BMIR)

Boussard Lab

My research area have focused on making sense of unconventional and complex wide data biological sets, such as signaling pathways, gene interactions, or more recently, Electronical Health Records (EHRs). In Boussard Lab, my research is to establish different novel strategies for assessing the quality of healthcare delivery, involving advanced machine learning methods and Natural Language Processing (NLP) methods.

I received my PhD from University of Rennes 1, where I was working in the Dyliss team (DYnamics, Logics and Inference for biological Systems and Sequences), at the INRIA institute (Rennes, France). During my PhD, my research was oriented towards the analysis of TGF-Beta protein effects particularly in tumoral tissues. That has led to a global and systematic analysis of high-dimensional signaling model. The challenge was to find biological relevance in heterogeneous and strongly connected data. To solve this problem, I established different data mining methods: clustering methods (Shared Nearest Neighbor), Formal Concepts Analysis, topological graph analysis and Semantic Web methods (with Gene Ontology).

Publications

Comparison of OrthogonalNLP Methods for Clinical Phenotyping and Assessment of Bone Scan Utilization among Prostate Cancer Patients

Coquet J, Bozkurt S, Kan K, Ferrari M, Blayney DW, Brooks JD, Hernandez-Boussard T

Journal of Biomedical Informatics (JBI), 94, pp 103184. 2019
‹10.1016/j.jbi.2019.103184›
KaSa: A Static Analyzer for Kappa

Boutillier P, Camporesi F, Coquet J, et al

Computational Methods in Systems Biology. Springer International Publishing, pp 285–291. 2018
‹10.1007/978-3-319-67471-1_6›
Identifying Functional Families of Trajectories in Biological Pathways by Soft Clustering: Application to TGF-β Signaling

Coquet J, Theret N, Legagneux V, Dameron O

Computational Methods in Systems Biology. Springer International Publishing, pp 91–107. 2017
‹10.1007/978-3-319-67471-1_6›
The smell of us – crowdsourcing human body odor evaluation

Benony M, Cardon M, Ferré A, Coquet J, et al

Human Computation, 3, pp 161–179. 2016
‹10.1007/978-3-319-67471-1_6›
Topological and semantic Web based method for analyzing TGF-β signaling pathways

Coquet J, Andrieux G, Nicolas J, et al

JOBIM. 2015
‹hal-01242893›

Awards & Volunteer projects

Project Manager of the film festival "Sciences en Court[s]": The festival allows doctoral students to make short films explaining their thesis work in a playful and educational way. 2015 - 2017
Best New Application, International Genetically Engineered Machine (iGEM) competition: Developer of a crowdsourcing web-based application with Django framework that engages volunteer smellers to characterize human sweat samples. This application was made as part of the International Genetically Engineered Machine (iGEM) competition. 2014

Scientific Popularization

Short Films Festival "Sciences en Court[s]": Une rencontre percutante