Publications

A novel and efficient machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-andr'e Legault
Jason Hartford
Benoît J. Arsenault
Y. Archer
Yang
Joelle Pineau
Mendelian Randomization (MR) enables estimation of causal effects while controlling for unmeasured confounding factors. However, traditional… (voir plus) MR's reliance on strong parametric assumptions can introduce bias if these are violated. We introduce a new machine learning MR estimator named Quantile Instrumental Variable (IV) that achieves low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying Quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes in the UK Biobank. Employing various MR estimators and colocalization techniques that allow multiple causal variants, our analysis reveals that a genetically predicted reduction in sclerostin levels significantly increases heel bone mineral density and reduces the risk of osteoporosis, while showing no discernible effect on ischemic cardiovascular diseases. Quantile IV contributes to the advancement of MR methodology, and the case study on the impact of circulating sclerostin modulation contributes to our understanding of the on-target effects of sclerostin inhibition.
2024-01-31
medRxiv (prépublication)
doi.org
Blockwise Self-Supervised Learning at Scale
Shoaib Ahmed Siddiqui
David Scott Krueger
Yann LeCun
Stephane Deny
2024-01-30
TMLR (accepté)
doi.org
openreview.net
Integrating accompanying patients into clinical oncology teams: limiting and facilitating factors
Marie-Pascale Pomey
Jesseca Paquette
Monica Iliescu Nelea
Cécile Vialaron
Rim Mourad
Karine Bouchard
Louise Normandin
Marie‐Andrée Côté
Mado Desforges
Pénélope Pomey‐Carpentier
Israël Fortin
Isabelle Ganache
Catherine Régis
Zeev Rosberger
Danielle Charpentier
Marie-France Vachon
Lynda Bélanger
Michel Dorval
Djahanchah Philip Ghadiri
Mélanie Lavoie-Tremblay … (voir 5 de plus)
Antoine Boivin
Jean-François Pelletier
Nicolas Fernandez
Alain M. Danino
Michèle de Guise
2024-01-30
BMC Health Services Research (publié)
doi.org
PERFUMES: pipeline to extract RNA functional motifs and exposed structures
Arnaud Chol
Roman Sarrazin-Gendron
Éric Lécuyer
Mathieu Blanchette
Jérôme Waldispühl
Abstract Motivation Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D… (voir plus) structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. Results We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES’ usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. Availability and implementation PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).
2024-01-30
Bioinform. (publié)
doi.org
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal rob… (voir plus)ots. Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. Our RL-based controller incorporates a novel dual-history architecture, utilizing both a long-term and short-term input/output (I/O) history of the robot. This control architecture, when trained through the proposed end-to-end RL approach, consistently outperforms other methods across a diverse range of skills in both simulation and the real world.The study also delves into the adaptivity and robustness introduced by the proposed RL system in developing locomotion controllers. We demonstrate that the proposed architecture can adapt to both time-invariant dynamics shifts and time-variant changes, such as contact events, by effectively using the robot's I/O history. Additionally, we identify task randomization as another key source of robustness, fostering better task generalization and compliance to disturbances. The resulting control policies can be successfully deployed on Cassie, a torque-controlled human-sized bipedal robot. This work pushes the limits of agility for bipedal robots through extensive real-world experiments. We demonstrate a diverse range of locomotion skills, including: robust standing, versatile walking, fast running with a demonstration of a 400-meter dash, and a diverse set of jumping skills, such as standing long jumps and high jumps.
2024-01-30
ArXiv (prépublication)
doi.org
arxiv.org
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Shengchao Liu
Chengpeng Wang
Jiarui Lu
Weili Nie
Hanchen Wang
Zhuoxinran Li
Bolei Zhou
Jian Tang
2024-01-30
TMLR (accepté)
openreview.net
openreview.net
Asymmetric stimulus representations bias visual perceptual learning
Pooya Laamerad
Asmara Awada
Christopher C. Pack
Shahab Bakhtiari
The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and… (voir plus) color. Within each region there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are overrepresented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. But other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.
2024-01-29
Journal of Vision (publié)
doi.org
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Faraz Lotfi
Khalil Virji
Nicholas Dudek
Gregory Dudek
2024-01-29
ArXiv (prépublication)
doi.org
arxiv.org
Trait‐matching models predict pairwise interactions across regions, not food web properties
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
2024-01-29
Global Ecology and Biogeography (publié)
doi.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
Marie-Christine Par'e
Vasken Dermardiros
Antoine Lesage-Landry
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (voir plus) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.
2024-01-28
ArXiv (prépublication)
doi.org
arxiv.org
Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments
Dongjoon Lim
Changhyun Baek
Mathieu Blanchette
2024-01-26
iScience (publié)
doi.org
MiRGraph: A transformer-based feature learning approach to identify microRNA-target interactions by integrating heterogeneous graph network and sequence information
Pei Liu
Ying Liu
Jiawei Luo
Yue Li
MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression by targeting specific mRNAs. They can function as both tumor sup… (voir plus)pressors and oncogenes depending on the specific miRNA and its target genes. Detecting miRNA-target interactions (MTIs) is critical for unraveling the complex mechanisms of gene regulation and identifying therapeutic targets and diagnostic markers. There is currently a lack of MTIs prediction method that simultaneously performs feature learning on heterogeneous graph network and sequence information. To improve the prediction performance of MTIs, we present a novel transformer-based multi-view feature learning method, named MiRGraph. It consists of two main modules for learning the sequence and heterogeneous graph network, respectively. For learning the sequence-based feaature embedding, we utilize the mature miRNA sequence and the complete 3’UTR sequence of the target mRNAs to encode sequence features. Specifically, a transformer-based CNN (TransCNN) module is designed for miRNAs and genes respectively to extract their personalized sequence features. For learning the network-based feature embedding, we utilize a heterogeneous graph transformer (HGT) module to extract the relational and structural information in a heterogeneous graph consisting of miRNA-miRNA, gene-gene and miRNA-target interactions. We learn the TransCNN and HGT modules end-to-end by utilizing a feedforward network, which takes the combined embedded features of the miRNA-gene pair to predict MTIs. Comparisons with other existing MTIs prediction methods illustrates the superiority of MiRGraph under standard criteria. In a case study on breast cancer, we identified plausible target genes of an oncomir hsa-MiR-122-5p and plausible miRNAs that regulate the oncogene BRCA1.
2024-01-26
bioRxiv (prépublication)
doi.org