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    SYSTEMS AND METHODS FOR IDENTIFYING NOVEL AND DIVERGENT VIRUSES IN TRANSCRIPTOMES
    5.
    发明公开
    SYSTEMS AND METHODS FOR IDENTIFYING NOVEL AND DIVERGENT VIRUSES IN TRANSCRIPTOMES 审中-公开

    公开(公告)号：US20240221942A1

    公开(公告)日：2024-07-04

    申请号：US18392646

    申请日：2023-12-21

    Applicant: The Wistar Institute of Anatomy and Biology

    Inventor： Noam Auslander ,  Abdurrahman Elbasir

    IPC: G16H50/20 ,  G16B30/10

    CPC classification number: G16H50/20 ,  G16B30/10

    Abstract: Systems and methods for identifying viral sequences in a subject of a species are provided. Sequence reads not associated with a reference genome of the species are obtained from a biological sample from the subject. At least a portion of each respective sequence read is encoded into a corresponding vector representing all or a portion of the sequence of the respective sequence read, thereby obtaining a plurality of vectors. Each sequence read is assigned a corresponding scalar model score by inputting a vector, in the plurality of vectors, corresponding to the sequence read into a model. Those sequence reads having a corresponding scalar model score that satisfies a first threshold score are selected as contig seeds. The plurality of sequence reads are aligned to these contig seeds through common k-mer sequences thereby forming a plurality of contigs which, in turn, are used to identify viral sequences in the subject.

    PROCESSES FOR PREDICTING THERAPY BENEFITS
    10.
    发明公开
    PROCESSES FOR PREDICTING THERAPY BENEFITS 审中-公开

    公开(公告)号：US20240105303A1

    公开(公告)日：2024-03-28

    申请号：US18469813

    申请日：2023-09-19

    Applicant: The Wistar Institute of Anatomy and Biology

    Inventor： Noam Auslander ,  Andrew Patterson

    IPC: G16H20/10

    CPC classification number: G16H20/10

    Abstract: Described are systems and methods of predicting a response to a medical treatment in a subject. The systems and methods include the steps of selecting a set of mutations within at least one biological process, training a set of classifiers from the set of selected mutations via a training dataset, determining the performance level of each classifier via a validation dataset, applying a subset of high-performance level classifiers from the validation dataset via a test dataset, and predicting the response to the medical treatment based on the test dataset.

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