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1. 发明申请

WO2023059751A1 PREDICTING VARIANT PATHOGENICITY FROM EVOLUTIONARY CONSERVATION USING THREE-DIMENSIONAL (3D) PROTEIN STRUCTURE VOXELS 审中-公开
公开(公告)号：WO2023059751A1
公开(公告)日：2023-04-13
申请号：PCT/US2022/045824
申请日：2022-10-05
申请人： ILLUMINA, INC. , ILLUMINA CAMBRIDGE LIMITED
发明人： HAMP, Tobias , GAO, Hong , FARH, Kai-How
IPC分类号： G16B20/20
摘要： The technology disclosed relates to determining pathogenicity of nucleotide variants. In particular, the technology disclosed relates to specifying a particular amino acid at a particular position in a protein as a gap amino acid, and specifying remaining amino acids at remaining positions in the protein as non-gap amino acids, generating a gapped spatial representation of the protein that includes spatial configurations of the non-gap amino acids, and excludes a spatial configuration of the gap amino acid, determining an evolutionary conservation at the particular position of respective amino acids of respective amino acid classes based at least in part on the gapped spatial representation, and based at least in part on the evolutionary conservation of the respective amino acids, determining a pathogenicity of respective nucleotide variants that respectively substitute the particular amino acid with the respective amino acids in alternate representations of the protein.

2. 发明申请

WO2022020492A1 VARIANT PATHOGENICITY SCORING AND CLASSIFICATION AND USES THEREOF 审中-公开
公开(公告)号：WO2022020492A1
公开(公告)日：2022-01-27
申请号：PCT/US2021/042605
申请日：2021-07-21
申请人： ILLUMINA, INC.
发明人： GAO, Hong , FARH, Kai-How , MCRAE, Jeremy, Francis
IPC分类号： G16B20/20 , G16B40/20
摘要： Derivation and use of pathogenicity scores (206) for gene variants are described herein. Applications, uses, and variations of the pathogenicity scoring process include, but are not limited to, the derivation and use of thresholds (212, 218) to characterize a variant as pathogenic or benign, the estimation of selection effects associated with a gene variant, the estimation of genetic disease prevalence using pathogenicity scores (206), and the recalibration of methods used to assess pathogenicity scores (206).

3. 发明申请

WO2022020487A1 VARIANT PATHOGENICITY SCORING AND CLASSIFICATION AND USES THEREOF 审中-公开
公开(公告)号：WO2022020487A1
公开(公告)日：2022-01-27
申请号：PCT/US2021/042599
申请日：2021-07-21
申请人： ILLUMINA, INC.
发明人： GAO, Hong , FARH, Kai-How , MCRAE, Jeremy Francis
IPC分类号： G16B20/20 , G16B40/20
摘要： Derivation and use of pathogenicity scores (206) for gene variants are described herein. Applications, uses, and variations of the pathogenicity scoring process include, but are not limited to, the derivation and use of thresholds (212, 218) to characterize a variant as pathogenic or benign, the estimation of selection effects associated with a gene variant, the estimation of genetic disease prevalence using pathogenicity scores (206), and the recalibration of methods used to assess pathogenicity scores (206).

4. 发明申请

WO2023059752A1 PROTEIN STRUCTURE-BASED PROTEIN LANGUAGE MODELS 审中-公开
公开(公告)号：WO2023059752A1
公开(公告)日：2023-04-13
申请号：PCT/US2022/045825
申请日：2022-10-05
申请人： ILLUMINA, INC. , ILLUMINA CAMBRIDGE LIMITED
发明人： HAMP, Tobias , GAO, Hong , FARH, Kai-How
IPC分类号： G16B20/20
摘要： The technology disclosed relates to determining pathogenicity of nucleotide variants. In particular, the technology disclosed relates to specifying a particular amino acid at a particular position in a protein as a gap amino acid, and specifying remaining amino acids at remaining positions in the protein as non-gap amino acids. The technology disclosed further relates to generating a gapped spatial representation of the protein that includes spatial configurations of the non-gap amino acids, and excludes a spatial configuration of the gap amino acid, and determining a pathogenicity of a nucleotide variant based at least in part on the gapped spatial representation, and a representation of an alternate amino acid created by the nucleotide variant at the particular position.

5. 发明申请

WO2020081122A1 DEEP LEARNING-BASED TECHNIQUES FOR PRE-TRAINING DEEP CONVOLUTIONAL NEURAL NETWORKS 审中-公开
公开(公告)号：WO2020081122A1
公开(公告)日：2020-04-23
申请号：PCT/US2019/031621
申请日：2019-05-09
申请人： ILLUMINA, INC.
发明人： GAO, Hong , FARH, Kai-How , REDDY PADIGEPATI, Samskruthi
IPC分类号： G16B20/00 , G16B40/20
摘要： The technology disclosed includes systems and methods to reduce overfitting of neural network-implemented models that process sequences of amino acids and accompanying position frequency matrices. The system generates supplemental training example sequence pairs, labelled benign, that include a start location, through a target amino acid location, to an end location. A supplemental sequence pair supplements a pathogenic or benign missense training example sequence pair. It has identical amino acids in a reference and an alternate sequence of amino acids. The system includes logic to input with each supplemental sequence pair a supplemental training position frequency matrix (PFM) that is identical to the PFM of the benign or pathogenic missense at the matching start and end location. The system includes logic to attenuate the training influence of the training PFMs during training the neural network-implemented model by including supplemental training example PFMs in the training data.

6. 发明申请

WO2018039272A1 SEMANTIC DISTANCE SYSTEMS AND METHODS FOR DETERMINING RELATED ONTOLOGICAL DATA 审中-公开
标题翻译：用于确定相关本体数据的语义距离系统和方法
公开(公告)号：WO2018039272A1
公开(公告)日：2018-03-01
申请号：PCT/US2017/048072
申请日：2017-08-22
申请人： ILLUMINA, INC.
发明人： FARH, Kai-How , HAKENBERG, Jorg , KARANGUTKAR, Milan , CUI, Wenwu , GAO, Hong
IPC分类号： G06F17/30
CPC分类号： G06F16/248 , G06F7/02 , G06F16/24575 , G06F16/367
摘要： Systems, computer-implemented methods, and non-transitory computer readable media are provided for determining related ontological data. The disclosed systems may be configured to receive a first ontology and a second ontology, the first ontology and the second ontology comprising hierarchically organized ontological data. The disclosed systems may also be configured to receive an indication that a first ontological datum in the first ontology is equivalent to a second ontological datum in the second ontology, and a query for ontological data related to a third ontological datum subordinate to the first ontological datum. The disclosed systems may be configured to determine a first semantic distance between the third ontological datum and a fourth ontological datum in the second ontology satisfies a semantic distance criterion, and output the fourth ontological datum based on the determination that the first semantic distance satisfies the semantic distance criterion.
摘要翻译：提供了用于确定相关本体论数据的系统，计算机实现的方法和非暂时性计算机可读介质。所公开的系统可以被配置为接收第一本体和第二本体，第一本体和第二本体包括分层组织的本体数据。所公开的系统还可以被配置为接收第一本体中的第一本体基准等于第二本体中的第二本体基准的指示以及与第一本体基准附属的第三本体基准相关的本体数据的查询。所公开的系统可以被配置为确定第三本体基准和第二本体中的第四本体基准之间的第一语义距离满足语义距离标准，并且基于确定第一语义距离满足语义输出第四本体基准距离标准。

7. 发明申请

WO2023014913A1 DEEP LEARNING-BASED USE OF PROTEIN CONTACT MAPS FOR VARIANT PATHOGENICITY PREDICTION 审中-公开
公开(公告)号：WO2023014913A1
公开(公告)日：2023-02-09
申请号：PCT/US2022/039476
申请日：2022-08-04
申请人： ILLUMINA, INC.
发明人： CHEN, Chen , GAO, Hong , SUNDARAM, Laksshman S. , FARH, Kai-How
IPC分类号： G16B20/00 , G16B15/20 , G16B40/20
摘要： The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

8. 发明申请

WO2018039276A1 FEDERATED SYSTEMS AND METHODS FOR MEDICAL DATA SHARING 审中-公开
标题翻译：用于医学数据共享的联邦系统和方法
公开(公告)号：WO2018039276A1
公开(公告)日：2018-03-01
申请号：PCT/US2017/048076
申请日：2017-08-22
申请人： ILLUMINA, INC.
发明人： FARH, Kai-How , CHENG, Donavan , SHON, John , HAKENBERG, Jorg , BOLOTIN, Eugene , GEANEY, James Casey , GAO, Hong , CHENG, Pam , SINGH, Inderjit , ROCHE, Daniel , KARANGUTKAR, Milan
IPC分类号： G06F17/30 , G06F19/00
摘要： Systems, computer-implemented methods, and non-transitory computer readable media are provided for sharing medical data. The disclosed systems may be configured to create a first workgroup having a first knowledgebase. This first knowledgebase may be federated with a common knowledgebase, and with a second knowledgebase of a second workgroup. At least one of the first knowledgebase, common knowledgebase, and second knowledgebase may be configured to store data items comprising associations, signs, and evidence. The signs may comprise measurements and contexts, and the associations may describe the relationships between the measurements and contexts. The evidence may support these associations. The disclosed systems may be configured to receive a request from a user in the first workgroup, retrieve matching data items, and optionally then output to the user at least some of the retrieved matching data items. The request may comprise at least one of a first association and a first measurement.
摘要翻译：提供系统，计算机实现的方法和非暂时性计算机可读介质以共享医学数据。所公开的系统可以被配置为创建具有第一知识库的第一工作组。这第一个知识库可以与一个公共知识库和第二个工作组的第二个知识库联合。第一知识库，公共知识库和第二知识库中的至少一个可以被配置为存储包括关联，符号和证据的数据项。符号可以包括测量和背景，并且这些关联可以描述测量和背景之间的关系。证据可以支持这些协会。所公开的系统可以被配置为接收来自第一工作组中的用户的请求，检索匹配的数据项目，并且可选地然后向用户输出至少一些检索到的匹配数据项目。该请求可以包括第一关联和第一测量中的至少一个。

9. 发明申请

WO2023059750A1 COMBINED AND TRANSFER LEARNING OF A VARIANT PATHOGENICITY PREDICTOR USING GAPPED AND NON-GAPPED PROTEIN SAMPLES 审中-公开
公开(公告)号：WO2023059750A1
公开(公告)日：2023-04-13
申请号：PCT/US2022/045823
申请日：2022-10-05
申请人： ILLUMINA, INC. , ILLUMINA CAMBRIDGE LIMITED
发明人： HAMP, Tobias , GAO, Hong , FARH, Kai-How
IPC分类号： G16B20/20
摘要： The technology disclosed relates to training a pathogenicity predictor. In particular, the technology disclosed relates to accessing a gapped training set that includes respective gapped protein samples for respective positions in a proteome, accessing a non-gapped training set that includes non-gapped benign protein samples and non-gapped pathogenic protein samples, generating respective gapped spatial representations for the gapped protein samples, and generating respective non-gapped spatial representations for the non-gapped benign protein samples and the non-gapped pathogenic protein samples, training a pathogenicity predictor over one or more training cycles and generating a trained pathogenicity predictor, wherein each of the training cycles uses as training examples gapped spatial representations from the respective gapped spatial representations and non-gapped spatial representations from the respective non-gapped spatial representations, and using the trained pathogenicity classifier to determine pathogenicity of variants.

10. 发明申请

WO2023014912A1 TRANSFER LEARNING-BASED USE OF PROTEIN CONTACT MAPS FOR VARIANT PATHOGENICITY PREDICTION 审中-公开
公开(公告)号：WO2023014912A1
公开(公告)日：2023-02-09
申请号：PCT/US2022/039475
申请日：2022-08-04
申请人： ILLUMINA, INC.
发明人： CHEN, Chen , GAO, Hong , SUNDARAM, Laksshman S. , FARH, Kai-How
IPC分类号： G16B20/00 , G16B15/20 , G16B40/20
摘要： The technology disclosed relates to a variant pathogenicity prediction network. The variant pathogenicity classifier includes memory, a variant encoding sub-network, a protein contact map generation sub-network, and a pathogenicity scoring sub-network. The memory stores a reference amino acid sequence of a protein, and an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide. The variant encoding sub-network is configured to process the alternative amino acid sequence, and generate a processed representation of the alternative amino acid sequence. The protein contact map generation sub-network is configured to process the reference amino acid sequence and the processed representation of the alternative amino acid sequence, and generate a protein contact map of the protein. The pathogenicity scoring sub-network is configured to process the protein contact map, and generate a pathogenicity indication of the variant amino acid.

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