Pierre Luc Carrier

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

Raphaël Poujol

Jean-Christophe Grenier

Julie G Hussin

1 Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic st… (see more)udies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep learning architecture proven effective in handling high-dimensional data, to learn population ancestry from single nucleotide polymorphisms (SNPs) data using the populational Thousand Genomes Project dataset. Our results highlight the model’s ability to generalize to diverse populations in the CARTaGENE and Montreal Heart Institute biobanks and that predictions remain robust to high levels of missing SNPs. We show that, despite the lack of North African populations in the training dataset, the model learns latent representations that reflect meaningful population structure for North African individuals in the biobanks. To improve model transparency, we apply Saliency Maps, DeepLift, GradientShap and Integrated Gradients attribution techniques and evaluate their performance in identifying SNPs leveraged by the model. Using DeepLift, we show that model’s predictions are driven by population-specific signals consistent with those identified by traditional population genetics metrics. This work presents a generalizable and interpretable deep learning framework for genetic ancestry inference in large-scale biobanks with genetic data. By enabling more widespread genomic ancestry characterization in these cohorts, this study contributes practical tools for integrating genetic data into downstream biomedical applications, supporting more inclusive and equitable healthcare solutions.

2025-08-29

bioRxiv (preprint)

doi.org

Crystal-GFN: sampling materials with desirable properties and constraints

Mistal

Alex Hernandez-Garcia

Alexandre AGM Duval

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

openreview.net

Crystal-GFN: sampling crystals with desirable properties and constraints

Alex Hernandez-Garcia

Alexandre AGM Duval

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (see more)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.

2023-10-06

ArXiv (preprint)

doi.org

arxiv.org

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Abhinav Sharma

Nanor Minoyan

Soren Harnois Leblanc

Victor Schmidt

Pierre-Luc St. Charles

Akshay Patel

David Buckeridge … (see 9 more)

Joumana Ghosn

Yang Zhang

Bernhard Schölkopf

Jian Tang

Irina Rish

Christopher Pal

Joanna Merckx

Eilif B. Muller

Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and vari… (see more)ous digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

2020-10-01

OpenReview.net/Anonymous_Preprint (unknown)

doi.org

openreview.net

Diet Networks: Thin Parameters for Fat Genomics

Adriana Romero

Marie-Pierre Dubé

Julie G. Hussin

Yoshua Bengio

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (see more) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2016-12-31

ICLR.cc/2017/conference (poster)

doi.org

openreview.net

Mila Ventures Founder in Residence

TRAIL: Responsible AI for Professionals and Leaders

AI Advantage: Productivity in Public Service

Pierre Luc Carrier

Publications

Mila Ventures Founder in Residence

TRAIL: Responsible AI for Professionals and Leaders

AI Advantage: Productivity in Public Service

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Pierre Luc Carrier

Publications