“Graph as models in life sciences: Machine learning and integrative approaches” is a Fall School (mid-October 2021) supported by Digicosme (Labex) on bioinformatics and statistical/machine learning, with graph as a central theme.
The school aims to address different biological topics and focus on methodological aspects. It targets mainly doctoral and post-doctoral students (in France and beyond) in computer science and bioinformatics, but will be open to all without registration fees.
The school will provide young researchers the opportunity to learn new methodologies, present their own work and exchange with other scientists.
- Chloé-Agathe Azencott
- Simona Coco
- Serguei Grudinin
- Laurent Jabob
- Andrei Zinovyev
- Chloé-Agathe Azencott: ML for GWAS integrating biological networks
- Simona Coco: Statistical learning to infer structural evolution (DCA, RBMs…)
- Sergey Grudinin: Neural networks for protein structure analysis and prediction, CNN/GNN for protein structure and interaction prediction
- Laurent Jabob: Convolution/Recurrent Kernel Networks for biological sequence and graph-structured data, GWAS integrating De Brujn graph.
- Andrei Zinovyev: Structured learning for single-cell differentiation trajectories
- Flora Jay, CR CNRS, LISN
- Yann Ponty, DR CNRS, LIX
- Ariane Migault, Chargée de communication du Labex DigiCosme