Data Science for Life Sciences

Life and health sciences are changing rapidly: new technologies provide large-scale individual data (“omics” data) that can be related to health-related outcomes and medical imaging data. Identifying predisposition factors for disease and making predictions about health is a tremendous challenge for data scientists that we will address by conceiving original data science methods, algorithms, and software to relate omics data to health-related traits. This action is the WP2 of Grenoble Alpes Data Institute.

Activities
  • Data Challenge "Epigenetic & High-Dimension Mediation"
    from June 7, 2017 to June 9, 2017
    Progresses in high-throughput sequencing make it possible to study how epigenetic changes mediate the effect of environmental risk factors on diseases. Epigenetic changes are said to be mediators when they intervene in the causal pathway between environmental exposures and diseases.
  • From June 7 to 9, 30 researchers and students from all over Europe and from the USA contributed to the data challenge « Epigenetic & High-Dimension Mediation » in Aussois.
  • The aim of the summer school is to provide a comprehensive overview on software and statistical methods for detecting genes involved in local adaptation. Lectures and software demos will be given during the summer school.
  • Johan Montagnat from CNRS I3S Laboratory will give a talk on "Integration of heterogeneous data for the implementation of learning techniques". The event will take place on Thursday November, 30th at 11:30 am at GIN.
  • Cancer Heterogeneity Data Challenge
    from December 10, 2018 to December 14, 2018
    The Cancer Heterogeneity data challenge is part of a program dedicated to innovation in education. The aim of this program is to provide (i) analytical frameworks to bridge the gap between large dataset and personalized medicine in disease treatments and (ii) innovative pedagogical methods to train students and health professionals to big data analysis in health science.

Recruited


Raphael Bacher

Raphael Bacher is a research engineer in signal processing and data science. In December 2017, he was recruited by the Data Institute of the University Grenoble Alpes as a Research Engineer to develop collaborative tools for data science teachers and researchers (WP1 & 2).


Magali Richard

Magali Richard
is a computational biologist specialized in experimental and theoritical genetic. In January 2018, she was recruited by the Data Institute of the University Grenoble Alpes as a Junior Researcher in Data Science for Life Sciences.