Publications

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
David Buckeridge
Jian Tang
Archer Yang
Yue Li
2024-02-01
JACC: Advances (publié)
doi.org
Neural semantic tagging for natural language-based search in building information models: Implications for practice
Mehrzad Shahinmoghadam
Samira Ebrahimi Kahou
Ali Motamedi
2024-02-01
Computers in industry (Print) (publié)
doi.org
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Jin Guo
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (voir plus)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
2024-02-01
IEEE Transactions on Software Engineering (publié)
doi.org
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
Philip Whybra
Alex Zwanenburg
Vincent Andrearczyk
Roger Schaer
Aditya P. Apte
Alexandre Ayotte
Bhakti Baheti
Spyridon Bakas
Andrea Bettinelli
Ronald Boellaard
Luca Boldrini
Irene Buvat
Gary J. R. Cook
Florian Dietsche
Nicola Dinapoli
Hubert S. Gabryś
Vicky Goh
Matthias Guckenberger
Mathieu Hatt
Mahdi Hosseinzadeh … (voir 26 de plus)
Aditi Iyer
Jacopo Lenkowicz
Mahdi A. L. Loutfi
Steffen Löck
Francesca Marturano
Olivier Morin
Christophe Nioche
Fanny Orlhac
Sarthak Pati
Arman Rahmim
Seyed Masoud Rezaeijo
Christopher G. Rookyard
Mohammad R. Salmanpour
Andreas Schindele
Isaac Shiri
Emiliano Spezi
Stephanie Tanadini-Lang
Florent Tixier
Taman Upadhaya
Vincenzo Valentini
Joost J. M. van Griethuysen
Fereshteh Yousefirizi
Habib Zaidi
Henning Müller
Martin Vallières
Adrien Depeursinge
2024-02-01
Radiology (publié)
doi.org
The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review
Mohamed Elahmedi
Riya Sawhney
Elena Guadagno
Fabio Botelho
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (publié)
doi.org
Transposable elements regulate thymus development and function
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Côté
Juliette Humeau
Eric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen R. Richie
Sébastien Lemieux
Pierre Thibault
Claude Perreault
2024-02-01
bioRxiv (prépublication)
doi.org
Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis.
Waseem Abu-Ashour
Sherif Emil
Sister Mary Emil
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (publié)
doi.org
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