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

Claim 4:
5. 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,

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:
phrases corresponding to different locations in the overall sequence, wherein each of the phrases has a length k and includes a location- specific TR-based segment or an indel derivation thereof adjacent to a corresponding set of flanking texts, and
a repeated base unit and a segment length for each phrase, wherein the repeated base unit represents a text pattern that is repeated for the corresponding TR sequence and the segment length corresponds to a total number of characters for the corresponding TR sequence, and

wherein refining the initial set includes—
sorting the initial set according to the repeated base unit and the segment length;
based on the sorted result, identifying phrase groupings that each include adjacently arranged phrases with matching repeated base unit and matching segment length;
identifying duplicates based on the phrase groupings, wherein the duplicates include matching character patterns; and
removing the duplicates to generate the refined set of phrases that include the expected phrases and/or the derived phrases used to develop the ML model; and

generating a refined set of phrases based on removing duplicates from the initial set, wherein the duplicates represent matching character sequences that are associated with differing locations; 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.