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公开(公告)号：US11820800B2
公开(公告)日：2023-11-21
申请号：US17285033
申请日：2019-11-04
发明人： Zibo Chen , Scott Boyken , Sherry Bermeo , Robert A. Langan , David Baker
IPC分类号： C07K14/00 , C07K14/435 , G01N33/53 , G16B15/00 , G16B5/00 , B82Y10/00 , G06N3/00 , H03K19/00 , B82Y5/00 , C07K19/00 , G16B5/10 , G16B5/30 , G01N33/542
CPC分类号： C07K14/435 , B82Y5/00 , B82Y10/00 , C07K14/00 , C07K19/00 , G01N33/53 , G06N3/002 , G16B5/00 , G16B15/00 , H03K19/00 , C07K2319/00 , G01N33/542 , G16B5/10 , G16B5/30 , Y10S977/914
摘要： Disclosed herein are designed heterodimer proteins, monomeric polypeptides capable of forming heterodimer proteins, protein scaffolds including such polypeptides, and methods for using the heterodimer proteins and subunit polypeptides for designing logic gates.
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公开(公告)号：US11699503B2
公开(公告)日：2023-07-11
申请号：US16479211
申请日：2018-01-19
发明人： Matthew Spitzer , Lawrence Fong
CPC分类号： G16B25/10 , A61K38/193 , A61K39/3955 , C07K16/2818 , G01N33/505 , G16B5/10 , G16B45/00 , G01N2333/70596
摘要： Methods for identifying cancer patients amenable to anti-cancer immunotherapy are provided along with methods of monitoring cancer therapy. Also provided are methods of treating cancer patients amenable to anti-cancer immunotherapy. The methods involve determining the level of CD127 PD-1 T cells. The patients are treated with an immune checkpoint inhibitor, such as an anti-CTLA-4 antibody, e.g. ipilimumab.
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3.发明公开公开(公告)号：US20240296927A1
公开(公告)日：2024-09-05
申请号：US18601543
申请日：2024-03-11
发明人： Brian D. Athey , Gerald A. Higgins , Alex Ade , Alexandr Kalinin , Narathip Reamaroon , James S. Burns
IPC分类号： G16H20/10 , G16B5/00 , G16B5/10 , G16B30/00 , G16B40/00 , G16B45/00 , G16B50/10 , G16H50/20 , G16H50/50
CPC分类号： G16H20/10 , G16B5/00 , G16B5/10 , G16B30/00 , G16B40/00 , G16B45/00 , G16B50/10 , G16H50/20 , G16H50/50
摘要： Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies. Knowledge-based segmentation methods are used to deconstruct the pharmacogenomic network into its constituent efficacy and adverse event sub-networks for applications in clinical decision support, drug re-purposing, and in silico drug discovery.
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4.发明授权公开(公告)号：US11984208B2
公开(公告)日：2024-05-14
申请号：US16749694
申请日：2020-01-22
发明人： Brian D. Athey , Gerald A. Higgins , Alex Ade , Alexandr Kalinin , Narathip Reamaroon , James S. Burns
IPC分类号： G16H20/10 , G16B5/00 , G16B5/10 , G16B20/00 , G16B30/00 , G16B40/00 , G16B45/00 , G16B50/10 , G16H50/20 , G16H50/50
摘要： Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies. Knowledge-based segmentation methods are used to deconstruct the pharmacogenomic network into its constituent efficacy and adverse event sub-networks for applications in clinical decision support, drug re-purposing, and in silico drug discovery.
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公开(公告)号：US20240203528A1
公开(公告)日：2024-06-20
申请号：US18260066
申请日：2022-01-20
发明人： Holger KLAPPROTH , Sonja BEDNAR
摘要： Methods of identifying homologous genomic sequences that may be present in a sample utilizing virtual probes, arrays for distinguishing homologous genomic sequences, systems for distinguishing homologous genomic sequences, and probe molecules useful in the methods, arrays, and systems of the disclosure.
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公开(公告)号：US20240101615A1
公开(公告)日：2024-03-28
申请号：US18158384
申请日：2023-01-23
发明人： Zibo CHEN , Scott Boyken , Sherry Bermeo , Robert A. Langan , David Baker
IPC分类号： C07K14/435 , B82Y5/00 , B82Y10/00 , B82Y15/00 , C07K14/00 , C07K19/00 , G01N33/53 , G06N3/00 , G16B5/00 , G16B15/00 , H03K19/00
CPC分类号： C07K14/435 , B82Y5/00 , B82Y10/00 , B82Y15/00 , C07K14/00 , C07K19/00 , G01N33/53 , G06N3/002 , G16B5/00 , G16B15/00 , H03K19/00 , C07K2319/00 , G16B5/10
摘要： Disclosed herein are designed heterodimer proteins, monomeric polypeptides capable of forming heterodimer proteins, protein scaffolds including such polypeptides, and methods for using the heterodimer proteins and subunit polypeptides for designing logic gates.
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公开(公告)号：US20230272483A1
公开(公告)日：2023-08-31
申请号：US18071786
申请日：2022-11-30
发明人： Wan-Ping Lee , Devin Locke
IPC分类号： C12Q1/6886 , G16B5/00 , G16B30/00 , G16B5/10 , G16B30/10
CPC分类号： C12Q1/6886 , G16B5/00 , G16B5/10 , G16B30/00 , G16B30/10 , C12Q2600/156 , C12Q2600/158
摘要： The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient’s tumor mutation burden.
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