Machine Learning for Somatic Single Nucleotide Variant Detection in Cell-free Tumor Nucleic acid Sequencing Applications

摘要

Systems and methods are disclosed to detect single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject by generating training data with class labels; in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; training the machine learning unit; and applying the machine learning unit to detect the SNVs from somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.

主权项

1. A method for detecting single-nucleotide variations (SNVs) from somatic sources in a cell-free biological sample of a subject, comprising:
(a) generating training data with class labels; (b) in computer memory, generating a machine learning unit comprising one output for each of adenine (A), cytosine (C), guanine (G), and thymine (T) calls; and (c) training the machine learning unit with a training set of biological samples, wherein the trained machine learning unit is configured to detect the SNVs from the somatic sources in the cell-free biological sample of the subject, wherein the cell-free biological sample comprises a mixture of nucleic acid molecules from somatic and germline sources.