Yang et al., 2019 - Google Patents

MSPL: Multimodal self-paced learning for multi-omics feature selection and data integration

Yang et al., 2019

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Document ID
35485187965206189
Author
Yang Z
Xia L
Zhang H
Liang Y
Publication year
Publication venue
IEEE Access

External Links

Snippet

Rapid advances in high-throughput sequencing technology have led to the generation of a large number of multi-omics biological datasets. Integrating data from different omics provides an unprecedented opportunity to gain insight into disease mechanisms from …
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Classifications

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    • G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
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    • G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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