International Business Machines Corporation
A DEEP LEARNING APPROACH TO CORRELATE CELLULAR MORPHOLOGY AND GENETICS

Abstract:

Provided is a data-driven deep-learning based algorithm for synthetic biology applications that makes no assumptions and/or hypotheses on genotype-phenotype interactions. deep-learning based algorithm trains a neural network with morphological features from single genetic modifications and tests the neural network with morphological features from multiple genetic modifications. The trained and tested neural network uses a link between the morphological features caused by the single and multiple gene modifications as input and outputs a genotype-phenotype mapping highlighting perturbation subspaces. The genotype-phenotype mapping is used to select one or more genetic insults as a starting point to engineer cells in synthetic biology applications.