Methods and systems for stratifying patient cancer risk using computational oncology and molecular data

A computing system, comprising:

• one or more processors, and
• one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to:
• receive molecular data corresponding to a patient;
• process the molecular data using a machine learning model to determine the patient's molecular data risk,
• wherein the machine learning model is trained using a patient training dataset and a reference training data set,
• wherein the machine learning model uses univariate gene selection, RNA bias correction and multivariate gene selection to filter and correct its training data, and
• wherein the machine learning model includes a survival model;
• and
• generate a matched treatment strategy corresponding to the patient based upon the patient's molecular data risk.

A computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to:

• receive molecular data corresponding to a patient;
• process the molecular data using a machine learning model to determine the patient's molecular data risk,
• wherein the machine learning model is trained using a patient training dataset and a reference training data set,
• wherein the machine learning model uses univariate gene selection, RNA bias correction and multivariate gene selection to filter and correct its training data, and
• wherein the machine learning model includes a survival model;
• and
• generate a matched treatment strategy corresponding to the patient based upon the patient's molecular data risk.