Meyers RM, Bryan JG, McFarland JM, et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat Genet. 2017;49:1779-1784.
McFarland JM, Bondy AG, Cumming BG, Butts DA. High-resolution eye tracking using V1 neuron activity. Nature communications. 2014;5:4605.
McFarland JM, Cui Y, Butts DA. Inferring nonlinear neuronal computation based on physiologically plausible inputs. PLoS Computational Biology. 2013;9(7):e1003143.
James McFarland, Ph.D.
James McFarland is a data scientist II in the Cancer Program of the Broad Institute of MIT and Harvard, where he works on several different projects using large-scale chemical and genetic perturbation screens to better understand the “landscape” of different vulnerabilities exhibited by cancer cells. Part of his research involves developing statistical models of large-scale genetic perturbation screens, in order to better resolve the essentiality of each gene across cancer cell lines. He also works on building models for predicting which cancer cell lines will be sensitive to a given chemical/genetic perturbation based on their genomic and molecular features.
Prior to joining the Broad Institute in 2016, McFarland obtained his Ph.D. in physics from Brown University. He earned a bachelor’s degree in physics from Pomona College and subsequently spent several years doing research in computational neuroscience, analyzing recordings of neural activity to determine how neurons process visual information.
Contact James McFarland via email at firstname.lastname@example.org.