Rate My Professor Gregory Way

Way's research centers on biomedical data science, deep learning, and high-content microscopy to develop cell morphology as a systems biology readout for disease states, enhancing drug discovery and clinical decision-making. His lab applies these methods to pediatric cancer, Neurofibromatosis Type 1, cardiac fibrosis, and brain cancer, utilizing open science practices with public data and software. Key contributions include developing the Cytominer ecosystem, BioBombe, Tybalt, and Project Cognoma. Selected publications are 'Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas' (Cell Reports, 2018), 'Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic' (bioRxiv, 2021; published 2022), 'High-content microscopy reveals a morphological signature of defective NF1 Schwann cells' (eLife, 2023), and 'A field guide to cultivating computational biology' (PLoS Biology, 2021). With over 3,200 citations and an h-index of 17, his work influences genomic and morphological data analysis. Major awards include the American Association for Cancer Research Team Science Award (2020) and the Interstellar Beyond Award from the Japan Agency for Medical Research and Development (2024) for pyroptosis research in glioblastoma.