Columbia University

Tempus Labs, Inc.

Raphael A Pelossof

Michael J Jones

Martin Christian Stumpe

Nike Tsiapera Beaubier

Joshua S K Bell

Catherine Igartua

Joshuah Kapilivsky

Calvin McCarter

Timothy Taxter

Jackson Michuda

Kyle Ashley Beauchamp

Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.

Systems and methods for multi-label cancer classification

Systems, methods, and media that: implement a boosted classifier having a plurality of weak hypotheses that produce a classification, each of the plurality of weak hypotheses having at least one weight; receive testing data; receive at least one piece of training data subsequently to receiving the testing data; calculate corrective terms for correcting a sum of weights of correctly classified training data and a sum of weights of incorrectly classified training data; calculate the sum of weights of correctly classified training data and the sum of weights of incorrectly classified training data based on the corrective terms; modify the at least one weight of at least one of the plurality of weak hypotheses in response to the at least one piece of training data based on the sum of weights to produce modified weights; and classify the testing data based on the modified weights to produce a classification.

Systems, methods, and media for performing classification using a boosted classifier

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New York, NY, United States of America

Raphael A Pelossof