AI for Computional Biology and Bio-inspired AI

AI for the analysis of advanced biological data to
1) reveal complex biological processes
2) inspire innovative computational processes

Computational biology 

  • Molecules: mining conformational spaces of huge dimensions to reveal biological functions 
  • Networks: combining single cell atlases and interaction networks (protein, metabolic, genetic, signaling, etc.) to reveal molecular pathways 
  • Cells/tissues: 3D+t super-resolution/multispectral microscopy to reveal differentiation/development complexity 
  • Brain: neuron-to-brain integration to model brain activity & computational neuroscience 

Bio-inspired AI 

  • Neuronal level: spiking models to better understand neuronal dynamics 
  • Cognition: neuronal dynamics for the analysis of learning/perception/action sequences 
  • Simulation/electronics: brain models to provide new neuromorphic-biomimetic algorithms/architectures 


3IA Chair holders

3IA International Chair

David Wales - Solution Landscapes for Machine Learning

Professor David Wales ScD, FRSC, FRS - University of Cambridge.

We explore machine learning landscapes in the cost function parameter space, which isanalogous to the potential energy surface of a molecule as a function of atomic coordinates. Ongoing advances
in methodology developed in chemical physics, can therefore be immediately applied to ML solution landscapes.

Our objectives are to use these tools to design improved predictions, and apply them to problems in molecular science and health care. In particular, we seek improved machine learning tools for clinician diagnostic support, to provide earlier detection of the deteriorating (and improving) patient. Specific applications include prediction of readmission to intensive care, which represent a failure in down-transfer to the ward, and are often associated with patient mortality.

3IA Chairs awarded in 2019

Laure Blanc-Féraud (CNRS) - Imaging for biology 

Recent advances in microscope technology provide outstanding images that allow biologists to address fundamental questions. This project aims at developing new AI methods and algorithms for (i) novel acquisition setups for super resolution imaging, and (ii) extraction of valuable quantitative information from these large heterogeneous datasets. 

 Frédéric Cazals (Inria) - AIMS: Artificial intelligence for molecular studies 

By learning essential features of proteins and their complexes, we shall deliver biologically relevant information for large molecular systems on biologically relevant time scales, leveraging our understanding of biological functions at the atomic level, and providing key inputs for protein design and engineering, and protein interaction networks. 

Grégoire Malandain (Inria) - Deciphering morphogenesis

Analysing 3D+t series of microscopic images allows us to follow organism development at the cellular level, yielding vast amounts of data. Dedicated AI methods will be proposed to (i) extract information from image data, and (ii) help build a standardized template of a median developing individual.

Patricia Reynaud-Bouret (CNRS) - MEL: Modeling and estimating learning 

We are defining new probabilistic models and new estimation methods to understand the deformation of functional connectivity during learning in in vivo experiments. 

Ellen Van Obberghen-Schilling (Inserm) - AI-powered analysis of the tumor microenvironment 

Our project will integrate tissue imaging modalities and artificial intelligence-based analysis tools for a deeper understanding and control of cancer, targeting tumor microenvironment and on the role of the extracellular matrix (ECM) in carcinoma progression, spread and response to therapy.

3IA Chairs awarded in 2020

Pascal Barbry - Human Lung Atlas

Benoit Miramond (Université Côte d’Azur) - Bio inspired AI from neurosciences to embedded autonomous devices.

The research project seeks to draw on the structure and function of the biological brain to develop more energy-efficient AI methods and algorithms. 
The scientific approach ranges from neural dynamics to the emerging cognitive properties of these networks and ultimately to the design of embedded neuromorphic electronic circuits. 
The project will focus on building bridges between the NeuroMod neuroscience institute and the 3IA Cote d'Azur institute.