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US 2022/0093224 A1Published application
Machine-Learned Quality Control for Epigenetic Data
AssigneeFOXO Labs Inc.
InventorsNichole Rigby, Marc M. Maxmeister, Randal S. Olson, Brian H. Chen
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Abstract
High throughput quality control for epigenetic profiles associated with different subjects may include a pipeline that identifies as faulty epigenetic profile(s) from among a batch of epigenetic profiles and may additionally or alternatively includes a machine-learning (ML) model trained to identify the type of fault or condition that caused an epigenetic profile to be faulty. Such an ML model and techniques for training such an ML model are discussed herein.
Claims20
A method comprising:
- receiving a set of epigenetic profiles, wherein a first epigenetic profile of the set comprises a plurality of epigenetic values associated with different DNA loci;
- determining a plurality of representations associated with the set of epigenetic profiles, wherein determining the plurality of representations comprises determining a first representation associated with the first epigenetic profile;
- determining, by an embedding algorithm, embeddings of the plurality of representations, wherein determining the embeddings comprises determining a first embedding associated with the first representation;
- and
- determining, by an ML model and based at least in part on the embeddings, two or more clusters of embeddings, wherein:
- a first cluster of the two or more clusters is associated with a first indication that epigenetic profiles associated with the first cluster fails a portion of a quality control pipeline, the first cluster including the first representation and one or more other representations associated with other epigenetic profiles different than the first epigenetic profile;
- and
- a second cluster of the two or more clusters is associated with a second indication that epigenetic profiles associated with the second cluster are valid.
5 more elements
A system comprising:
- one or more processors;
- and
- a memory storing processor-executable instructions that, when executed by one or more processors, cause the system to perform operations comprising:
- receiving a set of epigenetic profiles, wherein a first epigenetic profile of the set comprises a plurality of epigenetic values associated with different DNA loci;
- determining a plurality of distributions associated with the set of epigenetic profiles, wherein determining the plurality of distributions comprises determining a first distribution associated with the first epigenetic profile;
- determining, by an embedding algorithm, embeddings of the plurality of distributions, wherein determining the embeddings comprises determining a first embedding associated with the first distribution;
- and
- determining, by an ML model and based at least in part on the embeddings, two or more clusters of embeddings, wherein:
- a first cluster of the two or more clusters is associated with a first indication that epigenetic profiles associated with the first cluster fails a portion of a quality control pipeline, the first cluster including the first distribution and one or more other distributions associated with other epigenetic profiles different than the first epigenetic profile;
- and
- a second cluster of the two or more clusters is associated with a second indication that epigenetic profiles associated with the second cluster are valid.
8 more elements
A non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- receiving a set of epigenetic profiles, wherein a first epigenetic profile of the set comprises a plurality of epigenetic values associated with different DNA loci;
- determining a plurality of distributions associated with the set of epigenetic profiles, wherein determining the plurality of distributions comprises determining a first distribution associated with the first epigenetic profile;
- determining, by an embedding algorithm, embeddings of the plurality of distributions, wherein determining the embeddings comprises determining a first embedding associated with the first distribution;
- and
- determining, by an ML model and based at least in part on the embeddings, two or more clusters of embeddings, wherein:
- a first cluster of the two or more clusters is associated with a first indication that epigenetic profiles associated with the first cluster fails a portion of a quality control pipeline, the first cluster including the first distribution and one or more other distributions associated with other epigenetic profiles different than the first epigenetic profile;
- and
- a second cluster of the two or more clusters is associated with a second indication that epigenetic profiles associated with the second cluster are valid.
5 more elements
Dependencies are inferred from the claim text.