Publications

SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation
Yasmin Jolasun
Kailu Song
Yumin Zheng
Jingtao Wang
Gregory Fonseca
David H. Eidelman
Jun Ding
Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity but remains costly, restricting its use… (see more) to small cohorts that often lack comprehensive clinical data, reducing translational relevance. In contrast, bulk RNA sequencing is scalable and cost-effective but obscures critical single-cell insights. We introduce SIDISH, a neural network framework that integrates the granularity of scRNA-seq with the scalability of bulk RNA-seq. Using a variational autoencoder, deep Cox regression, and transfer learning, SIDISH identifies high-risk cell populations while enabling robust clinical predictions from large-cohort data. Its in silico perturbation module identifies therapeutic targets by simulating interventions that reduce high-risk cells associated with adverse outcomes. SIDISH also generalizes to spatial transcriptomics, identifying high-risk cells and mapping them within their native tissue microenvironment. Applied across diverse diseases, SIDISH establishes the link between cellular dynamics and clinical phenotypes, facilitating biomarker discovery and precision medicine. By unifying single-cell insights with large-scale clinical data, SIDISH advances computational tools for disease risk assessment and therapeutic prioritization, offering an integrative and scalable approach to precision medicine. SIDISH integrates single-cell and bulk RNA sequencing data using deep learning to identify high-risk cell populations and prognostic biomarkers, enabling in silico perturbations that could guide precision therapeutics and advance personalized medicine.
2025-12-09
Nature Communications (published)
doi.org
Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery
Tian Bai
Peng Tang
Yuting Xu
Vladimir Svetnik
Bingjia Yang
Abbas Khalili
Xiang Yu
Archer Y. Yang
In drug discovery, the reliability of compound screening based on manual assessments is compromised by potential bias, while existing method… (see more)s lack robust risk control measures. To address these challenges, we introduced conformal selection as an enhanced approach to optimize the compound screening process with balanced risks and benefits. Leveraging conformal inference, our approach constructs p-values for each candidate molecule to quantify statistical evidence for selection. The final selection of molecules is determined by comparing these p-values against thresholds derived from multiple testing principles. Our approach offers rigorous control over the false discovery rate, ensuring validity independent of dataset size and requiring minimal assumptions. By avoiding the estimation of prediction errors required in previous approaches, our method achieves higher accuracy (power), thereby improving the ability to identify promising candidates. Furthermore, our method demonstrates superior computational efficiency. We validate these advantages through numerical simulations on real-world datasets.
2025-12-08
Journal of Chemical Information and Modeling (unknown)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-12-08
2025 IEEE 64th Conference on Decision and Control (CDC) (published)
doi.org
arxiv.org
Generalized certainty equivalence based policies in partially observable systems
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical… (see more) certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state-feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non-linear) partially observed stochastic systems and allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models with Lipschitz cost and dynamics, we derive upper bounds on the sub-optimality of certainty equivalent policies in terms of expected error of the proposed estimator. We present several examples to illustrate the results.
2025-12-08
IEEE Conference on Decision and Control (published)
doi.org
Low-Dimensional solutions for optimal control of network-coupled subsystems over a directed network
Mohamed-Amine Azzouz
Shuang Gao
Aditya Mahajan
In this paper, we investigate optimal control of network-coupled subsystems, where the coupling between the dynamics of the subsystems is re… (see more)presented by the adjacency or Laplacian matrix of a directed graph. Under the assumption that the coupling matrix is normal and the cost coupling is compatible with the dynamics coupling, we use the spectral decomposition of the coupling matrix to decompose the overall system into at most n systems with noise coupled dynamics and decoupled cost, where n is the size of the network. Furthermore, the optimal control input at each subsystem can be computed by solving n1 decoupled Riccati equations where n1 (n1 ≤ n) denotes the number of distinct eigenvalues of the coupling matrix, where complex conjugate pairs are not double-counted. A salient feature of the result is that the solution complexity depends on the number of distinct eigenvalues of the coupling matrix rather than the size of the network. Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale network-coupled subsystems.
2025-12-08
IEEE Conference on Decision and Control (published)
doi.org
Reference radiation selection is confirmed as a significant source of relative biological effectiveness variation for neutrons.
Laura C Paterson
Stephen Pecoskie
Farrah Norton
Norma Ybarra
J. Kildea
Richard B Richardson
2025-12-08
International Journal of Radiation Biology (published)
doi.org
Cell-type specific transcriptional modulation by psilocybin induces sustained plasticity in mouse medial prefrontal cortex
Heike Schuler
Delong Zhou
Chloé Savignac
Vedrana Cvetkovska
Yiu-Chung Tse
Juliet Meccia
Joëlle Lopez
Ashot S. Harutyunyan
Jiannis Ragoussis
Danilo Bzdok
Rosemary C. Bagot
Despite enormous interest in psychedelics for psychiatric interventions, potential underlying biological mechanisms remain unclear. Here, we… (see more) confirm that a single dose of psilocybin increases synaptic transmission in mouse medial prefrontal cortex. Using scRNA-sequencing, we identify cell-type specific mechanisms of sustained neuroplastic effects. We show that, 24h post-psilocybin, expression of plasticity-related genes is increased in excitatory neurons and that transcription in a type of deep layer near projecting neuron, L5/6 NP, is robustly altered. Analyzing receptor expression patterns reveals that this cell-type specificity does not align with 5-HT 2A expression but aligns with 5-HT 2C expression patterns. Further, multivariate analyses identify psilocybin-induced gene expression patterns in L5/6 NP neurons predict 5-HT 2C , but not 5-HT 2A , transcript levels. Pharmacologic manipulation with a 5-HT 2C antagonist attenuates the post-acute sustained effect of psilocybin on synaptic transmission, highlighting 5-HT 2C signaling and L5/6 NP neurons as key mediators of psychedelic drug action’s sustained neuroplastic effects in mPFC.
2025-12-07
bioRxiv (preprint)
doi.org
SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs
Adrien Osakwe
Wenqi Dong
Qihuang Zhang
Robert Sladek
Yuemei Li
Spatial transcriptomics has revolutionized our ability to characterize tissues and diseases by contextualizing gene expression with spatial … (see more)organization. Available methods require researchers to either train a model using histology-based annotations or use annotation-free clustering approaches to uncover spatial domains. However, few methods provide researchers with a way to jointly analyze spatial data from both annotation-free and annotation-guided perspectives using consistent inductive biases and levels of interpretability. A single framework with consistent inductive biases ensures coherence and transferability across tasks, reducing the risks of conflicting assumptions. To this end, we propose the Spatial Topic Model (SpaTM), a topic-modeling framework capable of annotation-guided and annotation-free analysis of spatial transcriptomics data. SpaTM can be used to learn gene programs that represent histology-based annotations while providing researchers with the ability to infer spatial domains with an annotation-free approach if manual annotations are limited or noisy. We demonstrate SpaTM’s interpretability with its use of topic mixtures to represent cell states and transcriptional programs and how its intuitive framework facilitates the integration of annotation-guided and annotation-free analyses of spatial data with downstream analyses such as cell type deconvolution. Finally, we demonstrate how both approaches can be used to extend the analysis of large-scale snRNA-seq atlases with the inference of cell proximity and spatial annotations in human brains with Major Depressive Disorder.
2025-12-07
Briefings in Bioinformatics (published)
doi.org
International AI Safety Report Second Key Update: Technical Safeguards and Risk Management
Yoshua Bengio
Stephen Clare
Carina Prunkl
Maksym Andriushchenko
BEN BUCKNALL
Philip Fox
Nestor Maslej
Conor McGlynn
Malcolm Murray
Shalaleh Rismani
Stephen Casper
Jessica Newman
Daniel Privitera
Sören Mindermann
Daron Acemoglu
Thomas G. Dietterich
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Susan Leavy … (see 17 more)
Teresa Ludermir
Vidushi Marda
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Sarvapali D. (Gopal) Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
This is the Second Key Update to the 2025 International AI Safety Report. The First Key Update (1) discussed developments in the capabilitie… (see more)s of general-purpose AI models and systems and associated risks. This Key Update covers how various actors, including researchers, companies, and governments, are approaching risk management and technical mitigations for AI. The past year has seen important developments in AI risk management, including better techniques for training safer models and monitoring their outputs. While this represents tangible progress, significant gaps remain. It is often uncertain how effective current measures are at preventing harms, and effectiveness varies across time and applications. There are many opportunities to further strengthen existing safeguard techniques and to develop new ones. This Key Update provides a concise overview of critical developments in risk management practices and technical risk mitigation since the publication of the 2025 AI Safety Report in January. It highlights where progress is being made and where gaps remain. Above all, it aims to support policymakers, researchers, and the public in navigating a rapidly changing environment, helping them to make informed and timely decisions about the governance of general-purpose AI. Professor Yoshua BengioUniversité de Montréal / LawZero /Mila – Quebec AI Institute & Chair
2025-12-06
SuperIntelligence - Robotics - Safety & Alignment (published)
doi.org
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin
Andrei Cristian Nica
Mikhail Galkin
Jarrid Rector-Brooks
Kin Long Kelvin Lee
Santiago Miret
Frances H. Arnold
Michael M. Bronstein
Avishek Bose
Alexander Tong
Cheng-Hao Liu
2025-12-06
ArXiv (preprint)
doi.org
arxiv.org
Large Language Model Applications in the Algebra Domain: A Systematic Review
Yajie Song
Yimei Zhang
Doina Precup
Reihaneh Rabbany
Maria Cutumisu
2025-12-05
Technology, Knowledge and Learning (published)
doi.org
Longitudinal effects of cerebrovascular reactivity and cerebral pulsatility in cognitively intact older adults with APOE4: links with cognition
Zacharie Potvin-Jutras
Pierre-Luc Tremblay
Hanieh Mohammadi
Sylvia Villeneuve
R. Nathan Spreng
Claudine Gauthier
Sylvia Villeneuve
Judes Poirier
John C.S. Breitner
Jennifer Tremblay‐Mercier
Jordana Remz
Jean‐Michel Raoult
Yara Yakoub
Jonathan Gallego-Rudolf
Ting Qiu
Alfonso Fajardo Valdez
Béry Mohammediyan
Mohammadali Javanray
Amelie Metz
Safa Sanami … (see 30 more)
Valentin Ourry
Alfie Wearn
Adeline Bernier
Manon Edde
Julie Gonneaud
Cherie Strikwerda‐Brown
Christine Tardif
Claudine Gauthier
Maxime Descoteaux
Mahsa Dadar
Étienne Vachon‐Presseau
Andrée‐Ann Baril
Simon Ducharme
Maxime Montembeault
Maiya R. Geddes
Jean‐Paul Soucy
Natasha Rajah
Robert Laforce
Christian Bocti
Christos Davatzikos
Lune Bellec
Pedro Rosa‐Neto
Sylvain Baillet
Alan C. Evans
D. Louis Collins
M. Mallar Chakravarty
Kaj Blennow
Henrik Zetterberg
R. Nathan Spreng
Alexa Pichet Binette
2025-12-05
GeroScience (published)
doi.org