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公开(公告)号:US20200370130A1
公开(公告)日:2020-11-26
申请号:US16959806
申请日:2019-01-04
申请人: VISIONGATE, INC.
发明人: Daniel J. Sussman , Michael Meyer G. Meyer , Laimonis Kelbauskas , Alan C. Nelson , Randall Mastrangelo
IPC分类号: C12Q1/6886 , G16B20/50 , G16B40/20
摘要: A method to develop one or more morphometric classifiers to identify a tumor mutation burden (TMB). The method provides a non-invasive method of characterizing TMB that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.
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公开(公告)号:US20230050322A1
公开(公告)日:2023-02-16
申请号:US17968668
申请日:2022-10-18
申请人: VisionGate, Inc.
发明人: Michael G. Meyer , Daniel J. Sussman , Rahul Katdare , Laimonis Kelbauskas , Alan C. Nelson , Randall Mastrangelo
IPC分类号: G16B40/20 , G06T7/194 , G06T7/11 , G01N15/14 , G06K9/62 , G06T7/00 , G06V10/40 , G06V20/64 , G06V20/69
摘要: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.
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公开(公告)号:US11545237B2
公开(公告)日:2023-01-03
申请号:US16650304
申请日:2018-09-26
申请人: VISIONGATE, INC.
发明人: Michael G. Meyer , Daniel J. Sussman , Rahul Katdare , Laimonas Kelbauskas , Alan C. Nelson , Randall Mastrangelo
IPC分类号: G06T7/00 , G16B40/20 , G06T7/194 , G06T7/11 , G01N15/14 , G06K9/62 , G06V10/40 , G06V20/64 , G06V20/69 , G01N15/10
摘要: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.
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公开(公告)号:US20210200987A1
公开(公告)日:2021-07-01
申请号:US16972552
申请日:2019-06-05
申请人: VISIONGATE, INC.
摘要: A method to develop one or more morphometric classifiers to identify a mismatch repair deficiency (MMRD). The method provides a non-invasive method of characterizing MMRD that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.
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