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

Claim 8:
9. 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; 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,
wherein developing the ML model includes:
receiving a DNA sample set that includes text strings representative of DNA data associated with (1) patients confirmed to have one or more types of cancer and (2) patients confirmed to be without cancer, wherein the text strings are associated with different sample read depths;
retaining a refined set of sample text strings having the sample read depths satisfying a minimum read depth threshold;
developing the ML model based on selecting the features and/or training using the refined set of DNA sample set;
calculating at least a cancer-free scale factor and a cancer-specific scale factor based on a normalization parameter; and
using the refined set of retained sample text strings, normalizing the different sample read depths for the cancer-confirmed text strings and the cancer-free text strings using the cancer-specific scale factor and the cancer-free scale factor, respectively.