COMPOSITION FOR OVERCOMING RESISTANCE TO EGFR-TARGETING AGENT

    公开(公告)号:US20190062375A1

    公开(公告)日:2019-02-28

    申请号:US16081342

    申请日:2017-03-28

    Abstract: The present invention relates to methods of overcoming the resistance to an EGFR (Epidermal Growth Factor Receptor)-targeting antibody through a peptide that binds specifically to neuropilin-1 (NRP1). Moreover, the present invention relates to a fusion antibody in which a peptide that binds specifically to NRP1 is fused to an EGFR-targeting antibody, and to a composition of overcoming the resistance to an EGFR-targeting antibody alone by co-administration of the EGFR-targeting antibody and an NRP1-binding peptide-fused Fc. In addition, the fusion antibody according to the present invention, in which the NRP1-specific binding peptide is fused to an EGFR-targeting antibody, overcomes the resistance to the EGFR-targeting antibody alone in pancreatic cancer. Furthermore, the fusion antibody, in which the NRP1-specific binding peptide is fused to the EGFR-targeting antibody, also overcomes resistance to the EGFR-targeting antibody alone even in lung cancer. Thus, the NRP1-specific binding-fused EGFR-targeting antibody according to the present invention may be highly effective in the treatment of various tumors resistant to EGFR-targeting antibody alone.

    METHOD AND APPARATUS FOR GENERATING ONE CLASS MODEL BASED ON DATA FREQUENCY

    公开(公告)号:US20180240016A1

    公开(公告)日:2018-08-23

    申请号:US15687773

    申请日:2017-08-28

    CPC classification number: G06N3/08 G06F7/523 G06N20/00

    Abstract: Provided is a method for generating a one class model based on a data frequency. The method for generating a one class model based on a data frequency includes: generating, by a machine learning apparatus, a plurality of spatial coordinates by arranging a plurality of learning data in corresponding coordinates in a feature space; classifying, by the machine learning apparatus, the plurality of spatial coordinates into a plurality of internal coordinates PI and a plurality of external coordinates PO based on a frequency of the learning data arranged in the respective spatial coordinates which belong to the plurality of spatial coordinates; and generating, by the machine learning apparatus, a one class model based on the plurality of internal coordinates PI based on mutual spatial distances of the plurality of external coordinates PO and the plurality of internal coordinates PI.

    METHOD AND APPARATUS FOR DETERMINING CODING UNIT DEPTH BASED ON HISTORY

    公开(公告)号:US20180152711A1

    公开(公告)日:2018-05-31

    申请号:US15819027

    申请日:2017-11-21

    Abstract: Provided is a history based CU depth determining method. A history based CU depth determining method according to an exemplary embodiment of the present disclosure is a method for determining depths of a plurality of coding units (CU) included in each of a plurality of coding tree units (CTU) which configures a frame of a video, including: dividing a plurality of previous CTUs of a plurality of previous frames which are the same position as a current CTU of a current frame into a plurality of areas to generate depth history information including information of a CU depth for each of the plurality of areas; determining a plurality of depth candidates for a CU depth for each of the plurality of areas of the current CTU, based on the depth history information; and selecting an optimal CU depth among the plurality of depth candidates for each of the plurality of areas of the current CTU, through a rate-distortion cost (RD-cost) calculation.

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