Machine learning models for genomic predictive data analysis
AssigneeOptum, Inc.
InventorsRakhee Srivastava, Vivek Kumar Yadav, Pranav Mahajan, Lalit Singla
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Abstract
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing genomic predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform genomic predictive data analysis operations by using at least one of viral genomic processing machine learning models and bacterial genomic processing machine learning models.
Claims20
A computer-implemented method for performing predictive genomic analysis, the computer-implemented method comprising:
- identifying, using a processor, a first genomic sequence set;
- for each first genomic sequence in the first genomic sequence set:
- determining, using the processor and a frequency-based k-mer extraction layer of a viral genome processing machine learning model, and based at least in part on the first genomic sequence, one or more frequent k-mers of the first genomic sequence;
- and
- determining, using the processor and a one-dimensional convolutional neural network layer of the viral genome processing machine learning model, and based at least in part on the frequency-based refined representation, a viral replication origin k-mer of the one or more frequent k-mers;
- and
- performing, using the processor, one or more prediction-based actions based at least in part on each viral replication origin k-mer.
An apparatus for performing predictive genomic analysis, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:
- identify a first genomic sequence set;
- for each first genomic sequence in the first genomic sequence set:
- determine, using a frequency-based k-mer extraction layer of a viral genome processing machine learning model and based at least in part on the first genomic sequence, one or more frequent k-mers of the first genomic sequence;
- and
- determine, using a one-dimensional convolutional neural network layer of the viral genome processing machine learning model and based at least in part on the frequency-based refined representation, a viral replication origin k-mer of the one or more frequent k-mers;
- and
- perform one or more prediction-based actions based at least in part on each viral replication origin k-mer.
A computer program product for performing predictive genomic analysis, the computer program product comprising at least one non-transitory computer readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
- identify a first genomic sequence set;
- for each first genomic sequence in the first genomic sequence set:
- determine, using a frequency-based k-mer extraction layer of a viral genome processing machine learning model and based at least in part on the first genomic sequence, one or more frequent k-mers of the first genomic sequence;
- and
- determine, using a one-dimensional convolutional neural network layer of the viral genome processing machine learning model and based at least in part on the frequency-based refined representation, a viral replication origin k-mer of the one or more frequent k-mers;
- and
- perform one or more prediction-based actions based at least in part on each viral replication origin k-mer.
Dependencies are inferred from the claim text.