Patent ID: 11935627
Assignee: MUJIN, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:
generating text phrases that represent different DNA sequences,
wherein the text phrases include—
expected phrases corresponding to multiple locations in an overall genome, wherein phrases corresponding to each location include different combinations of flanking texts adjacent to a text segment that represents a tandem repeat (TR) sequence associated with the corresponding location, and
derived phrases representative of sampled mutations in the TR sequence, and

wherein generating the text phrases includes refining an initial set of segments and/or phrases based on removing overlaps and/or duplicates therein to generate the text phrases,
wherein the initial set includes:
segments representative of TR sequences that each include N number of adjacently repeated base units, wherein the repeated base units have a base unit length uniform across the segments, and
a location for each of the segments, wherein the location represents a position of the corresponding TR sequence within the overall genome, and

wherein refining the initial set includes—
sorting the initial set according to locations;
based on the sorted result, calculating a separation in location values between adjacently occurring segments;
identifying the adjacently occurring segments as the overlaps when the separation in location values is less than the base unit length;
removing one or more segments in the overlaps according to a predetermined pattern; and
generating a refined set of segments and/or phrases based on removing the overlaps from the initial set, wherein the overlaps represent TR sequences that are within the base unit length from each other; and

developing a machine learning (ML) model based on using a subset of the text phrases as features, wherein the ML model is trained and configured to compute a cancer signature based on analyzing text-based patient DNA data according to representations therein of mutations in patient DNA, the cancer signature representing (1) a likelihood that a corresponding patient has developed one or more types of cancer, (2) a likelihood that the patient will develop the one or more types of cancer within a given duration, (3) a development status at least leading up to onset of the one or more types of cancer, (4) monitoring a progression or a treatment response of the one or more types of cancer, or a combination thereof.