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US 2021/0202033 A1Published application
Novel machine learning approach for the identification of genomic features associated with epigenetic control regions and transgenerational inheritance of epimutations
AssigneeWashington State University
InventorsMichael K. Skinner, Md. Muksitul Haque
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Abstract
A two-step (sequential) machine learning analysis tool is provided that involves a combination of an initial active learning step followed by an imbalance class learner (ACL-ICL) protocol. This technique provides a more tightly integrated approach for a more efficient and accurate machine learning analysis. The combination of ACL and ICL work synergistically to improve the accuracy and efficiency of machine learning and can be used with any type of dataset including biological datasets.
Claims23
A method comprising:
- (a) obtaining sequencing data from a plurality of nucleotide sequences obtained from a sample from a person having a condition or suspect of having said condition, wherein a nucleotide sequence of the plurality of nucleotide sequences comprises a first plurality of genomic features and one or more differential DNA methylation regions (DMRs);
- (b) iteratively training an algorithm, using one or more computer processors, with said sequencing data to obtain a trained algorithm with a second plurality of genomic features, wherein said algorithm comprises active learning (AL);
- and
- (c) applying the trained algorithm to at least a portion of a nucleotide sequence to identify a DMR.
1 more element
Dependencies are inferred from the claim text.