Approaches to reducing dimensionality of genetic information used for machine learning and systems for implementing the same
AssigneeAIONCO, Inc.
InventorsCheuk Ying Tang, Victor Solovyev, Gene Lee
Life of this patent
Estimating the term…
Abstract
Introduced here is an approach to further refining a set of locations that can serve as inputs to a machine learning algorithm. These locations may refer to unique molecular positions in a reference human genome and/or unique mutations thereof that are relevant in diagnosing cancer. A computing system can determine the diagnostic relevance of each location and then discard some of the less diagnostically relevant locations.
Claims19
A method comprising:
- receiving an input indicative of an instruction to train a neural network to identify text phrases that are representative of mutations that are diagnostically relevant for a given type of cancer,
- wherein each of the text phrases is representative of a different set of characters, each of which is representative of a nucleotide;
- accessing a dataset that includes genetic information of multiple individuals that are known to have the given type of cancer;
- generating a first set of locations by examining the dataset,
- wherein each of the locations included in the first set is representative of a different molecular position at which a mutation is discovered through analysis of the genetic information;
- producing a set of metrics by computing, for each of the locations included in the first set, a metric that is indicative of correlation with the given type of cancer, as determined based on an analysis of the genetic information of the multiple individuals;
- generating a second set that includes fewer locations than the first set by
- identifying the locations included in the first set in order based on the set of metrics, ordered from most correlated with the given type of cancer to least correlated with the given type of cancer, and
- filtering at least some of the locations included in the first set, so as to produce the second set;
- and
- training the neural network using the second set.
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:
- receiving input indicative of an instruction to train a machine learning (ML) algorithm to produce a model that is able to identify mutations that are indicative of a given type of cancer;
- accessing genomic data associated with one or more individuals that are known to have the given type of cancer;
- generating a first list of locations by examining the genomic data,
- wherein each location included in the first list is representative of a different molecular position at which a mutation is discovered through analysis of the genomic data;
- reducing the first list through systematic analysis of correlation of each location with the given type of cancer, as determined based on an analysis of the genomic data, so as to produce a second list that has fewer locations than the first list;
- and
- storing the second list in a data structure that is associated with the given type of cancer.
Dependencies are inferred from the claim text.