Maria‐Anna Trapotsi

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

FlaHMM automates the identification of piRNA clusters in Drosophila species using hidden Markov models and genome sequence data alone.

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

This paper develops PKSmart, a computational model to predict human pharmacokinetic parameters using molecular features and animal PK data, achieving performance comparable to industry models.

The paper identifies five additional flam-like piRNA clusters across 119 Drosophila species, showing conserved transposon silencing mechanisms in somatic ovarian cells.

The paper develops similarity-based merger models that combine chemical structure and cell morphology predictions to improve assay hit calls across multiple biological assays.

The paper combines chemical structure, gene expression, and cell morphology data to improve mitochondrial toxicity prediction in drug discovery.