Chairs | AI for Computational Biology and Bio-inspired AI

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 International Chair 

2019


 

David Wales (University of Cambridge) 

Solution landscapes for Machine Learning

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 Chair holders

CHAIRS 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. 

CHAIRS 2020

Pascal Barbry (CNRS) 

Human Lung Atlas

The project elaborates on state-of-the-art approaches in genomics and cell biology to describe complex biological samples at the single-cell resolution. Multidimensional biological experiments result in large scale descriptions of DNA, RNA and protein expressions that can be integrated in time and space. The project aims at: (1) developing novel data-mining approaches based on machine learning and AI; (2) apply them to the study of the normal and pathological lung, in the context of serious threats that touch this organ (COVID-19, asthma, cystic fibrosis, cancer,...).

Benoît 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.

3IA Affiliate Chair

2020

David Rouquie (Université Côte d'Azur) | Bayer

Human health chemical risk assessments

The main focus of my research activity as affiliate chair at 3IA Côte d’Azur is on the emerging theme of chemical safety by design. This theme derives from all the on-going initiatives to improve the characterization of the risk of chemicals to humans by using more data from non-animal technologies and goes far beyond. Indeed, we are living a paradigm shift in the way bioactive small molecules are discovered but also de-risked. Instead of relying on numerous, long, costly cycles of trials and errors, thanks to the advance in systems biology and state-of-the-art machine learning algorithms it is possible to proactively drive de novo chemical design with high probability to induce specific biological responses. For the first time, we have shown as proof of concept that a learning procedure can automatically design molecules that have a high probability to induce a desired transcriptomic profile in cell lines. In my position of affiliate chair at 3IA Côte d’Azur, this approach will be further developed by building the pillars necessary to guide chemical design toward optimized safety profiles while maintaining the desired biological effect of the compounds.