MACHINE-LEARNING MODELS FOR SELECTING OLIGONUCLEOTIDE PROBES FOR ARRAY TECHNOLOGIES

    公开(公告)号:US20230340571A1

    公开(公告)日:2023-10-26

    申请号:US18307482

    申请日:2023-04-26

    CPC classification number: C12Q1/6811 C12Q1/6874 G16B25/20 C12Q2600/156

    Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning model to classify or predict a probability of an oligonucleotide probe yielding an accurate genotype call or hybridizing with a target oligonucleotide—based on the oligonucleotide probe's nucleotide-sequence composition. To intelligently identify oligonucleotide probes that are more likely to yield accurate downstream genotyping—or more likely to successfully hybridize with target oligonucleotides—some embodiments of the disclosed machine-learning model include customized layers trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy. By intelligently processing the nucleotide sequences of candidate oligonucleotide probes before implementing a microarray for a particular target oligonucleotide, the disclosed system can identify oligonucleotide probes with better genotyping accuracy (or better binding accuracy) than existing microarray systems for use in a microarray.

    METHODS AND SYSTEMS FOR MONITORING ORGAN HEALTH AND DISEASE

    公开(公告)号:US20210310067A1

    公开(公告)日:2021-10-07

    申请号:US17250814

    申请日:2020-01-22

    Applicant: Illumina, Inc.

    Inventor: Yong Li

    Abstract: Methods, compositions, and systems are provided for monitoring tissue and organ health. The methods, compositions, and systems provided herein extract locus specific copy number signals from cell free DNA (cfDNA) samples to identify tissue-specific cfDNA copy number profiles and enable quantification of tissue fractions in the cfDNA samples.

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