5 papers
Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets
Kevin Dradjat, Massinissa Hamidi, Blaise Hanczar
Accurate phenotype prediction from RNA sequencing (RNA-seq) data is essential for diagnosis, biomarker discovery, and personalized medicine. Deep learning models have demonstrated…
Self-supervised learning on gene expression data
Kevin Dradjat, Massinissa Hamidi, Pierre Bartet +1
Predicting phenotypes from gene expression data is a crucial task in biomedical research, enabling insights into disease mechanisms, drug responses, and personalized medicine. Trad…
Deep Generative Models for Discrete Genotype Simulation
Sihan Xie, Thierry Tribout, Didier Boichard +3
Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have pr…
IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability
Ahmad Fall, Federica Granese, Alex Lence +5
Monitoring and analyzing electrocardiogram (ECG) signals, even under varying physiological conditions, including those influenced by physical activity, drugs and stress, is crucial…
ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion
Alex Lence, Federica Granese, Ahmad Fall +4
In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signa…