Michael P. Menden

This study identifies miR-940 as a negative regulator of ferroptosis, showing it suppresses cell death by modulating key genes and GPX4 levels in cancer cells.

The paper develops a regression framework to identify pan-cancer single-gene biomarkers of drug sensitivity in cell line screens, recovering known markers and identifying new ones for ERK/MAPK inhibitors.

This study integrates single-cell spatial transcriptomics with bulk and single-cell RNA sequencing to reveal the molecular heterogeneity and mechanisms of atherosclerotic carotid artery plaques.

This study uses machine learning to improve metronidazole resistance prediction in Helicobacter pylori using whole-genome sequencing, achieving better accuracy than traditional methods.

The paper develops an interpretable multimodal machine learning framework to predict distal sensorimotor polyneuropathy using clinical and molecular data, identifying key biomarkers and improving prediction accuracy with additional molecular modalities.

This paper introduces GExMix, a data augmentation method for gene expression data, which improves drug response prediction by enhancing representation learning and prioritizing gene-centric information.

MetaIBS performs a large-scale meta-analysis of 16S rRNA amplicon data from IBS patients and healthy controls, identifying consistent microbial taxa differences.

The paper assesses molecular EMT signatures in cancer cell lines to identify drug susceptibilities, showing EMT can predict responses to HSP90 inhibitors and enhance drug sensitivity through TGF-β stimulation.

The paper develops workflows to predict drug synergy based on target functional similarity and validates them in breast and colorectal cancer cell lines.

The paper identifies subpopulations with differential drug responses using unsupervised machine learning to discover novel target populations and effective drug combinations.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.