Visiongate, Inc.

Randall Mastrangelo

Alan C Nelson

Michael G Meyer

Daniel J Sussman

Rahul Katdare

Laimonas Kelbauskas

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

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Gaithersburg, MD, United States of America

Randall Mastrangelo