Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification. - Edward De Brouwer; Jaak Simm; Adam Arany; Yves Moreau (CoRR, 2018)

GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series. - Edward De Brouwer; Jaak Simm; Adam Arany; Yves Moreau (NeurIPS, 2019)

Graph Informer Networks for Molecules. - Jaak Simm; Adam Arany; Edward De Brouwer; Yves Moreau (CoRR, 2019)

Longitudinal modeling of MS patient trajectories improves predictions of disability progression. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (CoRR, 2020)

Latent Convergent Cross Mapping. - Edward De Brouwer; Adam Arany; Jaak Simm; Yves Moreau (ICLR, 2021)

Expressive Graph Informer Networks. - Jaak Simm; Adam Arany; Edward De Brouwer; Yves Moreau (LOD, 2021)

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (Comput. Methods Programs Biomed., 2021)

The magnitude vector of images. - Michael F. Adamer; Leslie O'Bray; Edward De Brouwer; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2021)

Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series. - Jonghyeon Lee; Edward De Brouwer; Boumediene Hamzi; Houman Owhadi (CoRR, 2021)

Predicting the impact of treatments over time with uncertainty aware neural differential equations. - Edward De Brouwer; Javier Gonzalez; Stephanie L. Hyland (AISTATS, 2022)

Topological Graph Neural Networks. - Max Horn; Edward De Brouwer; Michael Moor; Yves Moreau; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Augmenting THerapeutic Effectiveness Through Novel Analytics (ATHENA) - A Public and Private Partnership Project Funded by the Flemish Government (VLAIO). - Ploingarm Petsophonsakul; Ashkan Pirmani; Edward De Brouwer; Murat Akand; Wouter Botermans; Frank Van Der Aa; Joris Robert Vermeesch; Fritz Offner; Roel Wuyts; Yves Moreau; Ingrid Maes; Ines Blockx; et al. (MIE, 2022)

Deep Counterfactual Estimation with Categorical Background Variables. - Edward De Brouwer (NeurIPS, 2022)

Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180]. - Edward De Brouwer; Thijs Becker; Yves Moreau; Eva Kubala Havrdova; Maria Trojano; Sara Eichau; Serkan Ozakbas; Marco Onofrj; Pierre Grammond; Jens Kuhle; Ludwig Kappos; Patrizia Sola; et al. (Comput. Methods Programs Biomed., 2022)

Predicting the impact of treatments over time with uncertainty aware neural differential equations. - Edward De Brouwer; Javier González Hernández; Stephanie L. Hyland (CoRR, 2022)

Learning predictive checklists from continuous medical data. - Yukti Makhija; Edward De Brouwer; Rahul G. Krishnan (CoRR, 2022)

Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. - Edward De Brouwer; Rahul G. Krishnan (ICLR, 2023)

Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection. - Martijn Oldenhof; Adam Arany; Yves Moreau; Edward De Brouwer (ICLR, 2023)

Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations. - Dhananjay Bhaskar; Daniel Sumner Magruder; Matheo Morales; Edward De Brouwer; Aarthi Venkat; Frederik Wenkel; Guy Wolf; Smita Krishnaswamy (LoG, 2023)

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. - Guillaume Huguet; Alexander Tong 001; Edward De Brouwer; Yanlei Zhang; Guy Wolf; Ian Adelstein; Smita Krishnaswamy (NeurIPS, 2023)

Inferring dynamic regulatory interaction graphs from time series data with perturbations. - Dhananjay Bhaskar; Daniel Sumner Magruder; Edward De Brouwer; Aarthi Venkat; Frederik Wenkel; Guy Wolf; Smita Krishnaswamy (CoRR, 2023)

Manifold Filter-Combine Networks. - Joyce A. Chew; Edward De Brouwer; Smita Krishnaswamy; Deanna Needell; Michael Perlmutter (CoRR, 2023)

BLIS-Net: Classifying and Analyzing Signals on Graphs. - Charles Xu 004; Laney Goldman; Valentina Guo; Benjamin Hollander-Bodie; Maedee Trank-Greene; Ian Adelstein; Edward De Brouwer; Rex Ying; Smita Krishnaswamy; Michael Perlmutter (AISTATS, 2024)

Benchmarking Observational Studies with Experimental Data under Right-Censoring. - Ilker Demirel; Edward De Brouwer; Zeshan M. Hussain; Michael Oberst; Anthony Philippakis; David A. Sontag (AISTATS, 2024)

Atom-Level Optical Chemical Structure Recognition with Limited Supervision. - Martijn Oldenhof; Edward De Brouwer; Adam Arany; Yves Moreau (CVPR, 2024)

Model Based Clustering of Time Series Utilizing Expert ODEs. - András Formanek; Edward De Brouwer; Péter Antal; Yves Moreau; Adam Arany (ICANN (4), 2024)

Convergence of Manifold Filter-Combine Networks. - David R. Johnson; Joyce A. Chew; Siddharth Viswanath; Edward De Brouwer; Deanna Needell; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2024)

Learning Predictive Checklists with Probabilistic Logic Programming. - Yukti Makhija; Edward De Brouwer; Rahul G. Krishnan (CoRR, 2024)

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma. - Zeshan M. Hussain; Edward De Brouwer; Rebecca Boiarsky; Sama Setty; Neeraj Gupta; Guohui Liu; Cong Li; Jaydeep Srimani; Jacob Zhang; Rich Labotka; David A. Sontag (npj Digit. Medicine, 2024)

The magnitude vector of images. - Michael F. Adamer; Edward De Brouwer; Leslie O'Bray; Bastian Rieck (J. Appl. Comput. Topol., 2024)

Modeling Complex System Dynamics with Flow Matching Across Time and Conditions. - Martin Rohbeck; Edward De Brouwer; Charlotte Bunne; Jan-Christian Huetter; Anne Biton; Kelvin Y. Chen; Aviv Regev; Romain Lopez (ICLR, 2025)

Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States. - Tianyu Liu 001; Edward De Brouwer; Tony Kuo; Nathaniel Diamant; Alsu Missarova; Hanchen Wang 002; Minsheng Hao; Héctor Corrada Bravo; Gabriele Scalia; Aviv Regev; Graham Heimberg (RECOMB, 2025)

Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data. - Ashkan Pirmani; Edward De Brouwer; Adam Arany; Martijn Oldenhof; Antoine Passemiers; Axel Faes; Tomas Kalincik; Serkan Ozakbas; Riadh Gouider; Barbara Willekens; Dana Horáková; Eva Kubala Havrdova; et al. (npj Digit. Medicine, 2025)

RAG-Enhanced Collaborative LLM Agents for Drug Discovery. - Namkyeong Lee; Edward De Brouwer; Ehsan Hajiramezanali; Chanyoung Park 001; Gabriele Scalia (CoRR, 2025)