PINS: A Perturbation Clustering Approach for Data Integration and Disease Subtyping

A disease subtyping/patient subgrouping computerized method based on the molecular profile of a patient, the computerized method comprising:

• receiving assay results of samples collected from a population of human subjects;
• generating a population dataset from the assay results that defines a molecular profile for the population;
• receiving an assay result of a patient sample;
• generating a patient dataset from the assay result that defines a molecular profile of the patient;
• and
• analyzing the population dataset by:
• a) acquiring the population dataset from the population of human subjects;
• b) performing an unsupervised cluster analysis on the population dataset to define clusters of the population, wherein each cluster represents a disease subtype or a patient subgroup;
• c) using machine learning techniques to act predetermined features of each disease subtype or patient subgroup;
• and
• d) finding a disease subtype or patient subgroup that represents the closest match to the molecular profile of the patient.

A system comprising:

• at least one processor and
• a memory coupled to the at least one processor;
• wherein the memory stores instructions for execution by the at least one processor, and wherein the instructions include:
• receiving assay results of samples collected from a population of human subjects;
• generating a population dataset from the assay results that defines a molecular profile for the population;
• receiving an assay result of a patient sample;
• generating a patient dataset from the assay result that defines a molecular profile of the patient;
• and
• analyzing the population dataset by:
• a) acquiring the population dataset from the population of human subjects;
• b) performing an unsupervised cluster analysis on the population dataset to define clusters of the population, wherein each cluster represents a disease subtype or a patient subgroup;
• c) using machine learning techniques to extract predetermined features of each disease subtype or patient subgroup;
• and
• d) finding a disease subtype or patient subgroup that represents the closest match to the molecular profile of the patient.