HUMANIZED MOUSE MODEL FOR CANCER METASTASIS
    3.
    发明申请

    公开(公告)号:US20190082664A1

    公开(公告)日:2019-03-21

    申请号:US16131256

    申请日:2018-09-14

    IPC分类号: A01K67/027 G01N33/50

    摘要: The present disclosure provides, in some aspects, a humanized mouse (NSG™-SGM3), engrafted with CD34+ hematopoietic progenitor cells and human metastatic melanoma cells, which surprisingly promotes secondary metastatic colonization, modeling the interplay between human tumors and the human immune system, and thus enabling mechanistic and pre-clinical studies for the development of novel treatment strategies targeting human-specific molecular pathways controlling melanoma dissemination.

    METHODS AND APPARATUS FOR IDENTIFYING ALTERNATIVE SPLICING EVENTS

    公开(公告)号:US20210233640A1

    公开(公告)日:2021-07-29

    申请号:US17256256

    申请日:2019-06-26

    IPC分类号: G16H20/40

    摘要: Methods and apparatus for identifying alternative splicing events. The method comprises receiving a dataset of percent spliced in (PSI) values for each of a plurality of biological samples, wherein the plurality of biological samples includes a first population of samples having a first characteristic and a second population of samples having a second characteristic different from the first characteristic, fitting, to the dataset, a probabilistic model to identify clusters of samples in the dataset, calculating cluster characteristics for each of the clusters, filtering the clusters based, at least in part, on the cluster characteristics to identify a subset of clusters, each of which is associated with an alternative splicing event, and storing on the at least one storage device, information associated with the identified alternative splicing events.

    Methods and apparatus for identifying alternative splicing events

    公开(公告)号:US12051496B2

    公开(公告)日:2024-07-30

    申请号:US17256256

    申请日:2019-06-26

    IPC分类号: G16H20/40

    CPC分类号: G16H20/40

    摘要: Methods and apparatus for identifying alternative splicing events. The method comprises receiving a dataset of percent spliced in (PS I) values for each of a plurality of biological samples, wherein the plurality of biological samples includes a first population of samples having a first characteristic and a second population of samples having a second characteristic different from the first characteristic, fitting, to the dataset, a probabilistic model to identify clusters of samples in the dataset, calculating cluster characteristics for each of the clusters, filtering the clusters based, at least in part, on the cluster characteristics to identify a subset of clusters, each of which is associated with an alternative splicing event, and storing on the at least one storage device, information associated with the identified alternative splicing events.