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cs.LG2026
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…
cs.LG2025
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…
cs.LG2023
On the Necessity of Metalearning: Learning Suitable Parameterizations for Learning Processes
Massinissa Hamidi, Aomar Osmani
In this paper we will discuss metalearning and how we can go beyond the current classical learning paradigm. We will first address the importance of inductive biases in the learnin…