Garrett Gaskins, PhD
Grad Student; Genentech Fellow; Hillblom Fellow, 2014-2021. High-content screening across varied cells, conditions, and chemical libraries has emerged as a key method for identifying novel compounds capable of systematically perturbing a biological network to induce a phenotype. The molecular targets (proteins, receptors, etc.) through which these compounds act to achieve their phenotype(s) are typically unknown. Garrett developed a chemoinformatic approach using aspects of image processing, machine learning, and SEA to identify the targets of novel compounds via their phenotypic signature.
Lab papers
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Machine-learning convergent melanocytic morphology despite noisy archival slides.
Tada M, Gaskins G, Ghandian S, Mew N, Keiser MJ, Keiser ES. bioRxiv. 2024 Sep 12.
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Predicted Biological Activity of Purchasable Chemical Space.
Irwin JJ, Gaskins G, Sterling T, Mysinger MM, Keiser MJ. J Chem Inf Model. 2018 Jan 22.
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Evolutionarily Conserved Roles for Blood-Brain Barrier Xenobiotic Transporters in Endogenous Steroid Partitioning and Behavior.
Hindle SJ, Munji RN, Dolghih E, Gaskins G, Orng S, Ishimoto H, Soung A, DeSalvo M, Kitamoto T, Keiser MJ, Jacobson MP, Daneman R, Bainton RJ. Cell Rep. 2017 Oct 31.