专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

WO2018094360A2 METHODS AND SYSTEMS FOR PREDICTING DNA ACCESSIBILITY IN THE PAN-CANCER GENOME 审中-公开
公开(公告)号：WO2018094360A2
公开(公告)日：2018-05-24
申请号：PCT/US2017/062626
申请日：2017-11-20
申请人： NANTOMICS, LLC , NANT HOLDINGS IP, LLC , WNUK, Kamil , SUDOL, Jeremi , RABIZADEH, Shahrooz
发明人： WNUK, Kamil , SUDOL, Jeremi , RABIZADEH, Shahrooz , SOON-SHIONG, Patrick , SZETO, Christopher , VASKE, Charles
IPC分类号： G06F19/24 , G06F19/12 , G06F19/26 , C12Q1/68
摘要： Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

2. 发明申请

WO2018094360A3 METHODS AND SYSTEMS FOR PREDICTING DNA ACCESSIBILITY IN THE PAN-CANCER GENOME 审中-公开
公开(公告)号：WO2018094360A3
公开(公告)日：2018-05-24
申请号：PCT/US2017/062626
申请日：2017-11-20
申请人： NANTOMICS, LLC , NANT HOLDINGS IP, LLC
发明人： WNUK, Kamil , SUDOL, Jeremi , RABIZADEH, Shahrooz , SOON-SHIONG, Patrick , SZETO, Christopher , VASKE, Charles
IPC分类号： G06F19/24 , G06F19/12 , G06F19/26 , C12Q1/68
摘要： Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

3. 发明申请

WO2018094360A9 METHODS AND SYSTEMS FOR PREDICTING DNA ACCESSIBILITY IN THE PAN-CANCER GENOME 审中-公开
公开(公告)号：WO2018094360A9
公开(公告)日：2018-05-24
申请号：PCT/US2017/062626
申请日：2017-11-20
申请人： NANTOMICS, LLC , NANT HOLDINGS IP, LLC
发明人： WNUK, Kamil , SUDOL, Jeremi , RABIZADEH, Shahrooz , SOON-SHIONG, Patrick , SZETO, Christopher , VASKE, Charles
IPC分类号： G06F19/24 , G06F19/12 , G06F19/26 , C12Q1/68
摘要： Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

4. 发明申请

WO2018017467A1 DISTRIBUTED MACHINE LEARNING SYSTEMS, APPARATUS, AND METHODS 审中-公开
标题翻译：分布式机器学习系统，装置和方法
公开(公告)号：WO2018017467A1
公开(公告)日：2018-01-25
申请号：PCT/US2017/042356
申请日：2017-07-17
申请人： NANTOMICS, INC. , NANT HOLDINGS IP, LLC.
发明人： SZETO, Christopher , BENZ, Stephen, Charles , WITCHEY, Nicholas, J.
IPC分类号： G06N99/00 , G06N5/04
摘要： A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
摘要翻译：
介绍了一个分布式在线机器学习系统。预期的系统包括许多私人数据服务器，每个服务器都有本地私有数据。研究人员可以要求相关私人数据服务器在他们的本地私人数据上训练机器学习算法的实现，而不要求取消私人数据的识别，也不要将私人数据暴露给未授权的计算系统。私人数据服务器还根据实际数据的数据分布生成合成数据或代理数据。服务器然后使用代理数据来训练代理模型。当代理模型与训练的实际模型足够相似时，可以将代理数据，代理模型参数或其他学习知识传输到一个或多个非私人计算设备。然后，可以将来自许多私人数据服务器的知识聚合到一个或多个受过训练的全球模型中，而不会泄露私人数据。

5. 发明申请

WO2018232320A2 PROGNOSTIC INDICATORS OF POOR OUTCOMES IN PRAEGNANT METASTATIC BREAST CANCER COHORT 审中-公开
公开(公告)号：WO2018232320A2
公开(公告)日：2018-12-20
申请号：PCT/US2018/037876
申请日：2018-06-15
申请人： NANTOMICS, LLC
发明人： SZETO, Christopher , BENZ, Stephen Charles , NGUYEN, Andrew
IPC分类号： G16H50/30 , G16H50/50 , G06F19/12 , C12Q1/6886
摘要： Transcriptomics data from tumor tissue of patients diagnosed with metastatic breast cancer are clustered and associated with overall survival of the patients. A subset of genes from one of the cluster associated with poor outcome are used to generate a survival prediction model predicting a survival time based on expression levels of a plurality of genes. Using such generated survival prediction model, a survival time of a patient diagnosed with metastatic breast cancer can be predicted and a treatment regimen can be updated or generated based on the survival time.

6. 发明申请

WO2020092101A1 CONSENSUS MOLECULAR SUBTYPES SIDEDNESS CLASSIFICATION 审中-公开
公开(公告)号：WO2020092101A1
公开(公告)日：2020-05-07
申请号：PCT/US2019/057732
申请日：2019-10-23
申请人： NANTOMICS, LLC
发明人： SZETO, Christopher , KAZMIERCZAK, Kevin , NGUYEN, Andrew , BENZ, Steve
IPC分类号： C12Q1/68 , C12Q1/6886 , G01N33/554
摘要： Contemplated systems and methods are directed to omics analyses in tumor tissue to provide a response prediction to treatment of CRC using immune therapy. Contemplated systems and methods are also directed towards methods of treating colorectal cancer (CRC) in a patient in need thereof, by determining consensus molecular subtypes (CMS) class, microsatellite instability (MSI) status, and sidedness of the CRC tumor; and treating CRC in the patient with immune therapy where the CMS class is CMS1, where the tumor exhibits MSI, and where sidedness is transverse sides.

7. 发明申请

WO2020091944A1 GENOMIC AND IMMUNE INFILTRATION DIFFERENCES BETWEEN MSI AND MSS GI TUMORS 审中-公开
公开(公告)号：WO2020091944A1
公开(公告)日：2020-05-07
申请号：PCT/US2019/054567
申请日：2019-10-03
申请人： NANTOMICS, LLC
发明人： NEWTON, Yulia , VASKE, Charles , SZETO, Christopher , REDDY, Sandeep K. , BENZ, Stephen Charles
IPC分类号： A61K35/17 , C12N5/0783 , C12Q1/68 , C12Q1/6886 , G01N33/50
摘要： Contemplated systems and methods are directed to omics analysis for GI cancers with respect to MSI status. Certain cancers had significant differences in their pathway activity, checkpoint gene expression, and expression of TME genes, and the inventors contemplate that such differences can be used to further improve treatment outcome prediction of cancers with regard to immune therapy.

8. 发明申请

WO2018222883A1 INVESTIGATING TUMORAL AND TEMPORAL HETEROGENEITY THROUGH COMPREHENSIVE -OMICS PROFILING IN PATIENTS WITH METASTATIC TRIPLE NEGATIVE BREAST CANCER 审中-公开
公开(公告)号：WO2018222883A1
公开(公告)日：2018-12-06
申请号：PCT/US2018/035410
申请日：2018-05-31
申请人： NANTOMICS, LLC
发明人： SZETO, Christopher , PARULKAR, Rahul , SEDGEWICK, Andrew
IPC分类号： C12Q1/68 , G06F19/00 , G01N33/574 , G06F17/30
摘要： Molecular profiles of metastatic tumors can be more accurately determined using combination of DNA mutational profiles and RNA expression profiles of selected genes that show substantial changes upon anti-tumor treatment. Such combinatorial information can be used to determine differential pathway activity that may be related to the sensitivity or resistance to the anti-tumor treatment.

9. 发明申请

WO2014210611A1 PATHWAY ANALYSIS FOR IDENTIFICATION OF DIAGNOSTIC TESTS 审中-公开
标题翻译：路线分析诊断诊断测试
公开(公告)号：WO2014210611A1
公开(公告)日：2014-12-31
申请号：PCT/US2014/044950
申请日：2014-06-30
申请人： NANTOMICS, LLC
发明人： BENZ, Stephen, Charles , RABIZADEH, Shahrooz , SZETO, Christopher , WEINGARTEN, Paul , TAO, Chunlin
IPC分类号： G06F19/10
CPC分类号： G16H50/20 , G06F19/00
摘要： The present inventive subject matter provides apparatus, systems, and methods in which a diagnostic test is identified, where the diagnostic test is for determining whether a particular treatment is effective for a particular patient based on one or more characteristics of a patient's cells. When a treatment is developed with the potential to treat one or more diseases, the drug can have different effects on different cell lines related to the diseases. A machine learning system is programmed to infer a measurable cell characteristic, out of many different measurable cell characteristics, that has a desirable correlation with the sensitivity data of different cell lines to a treatment. The machine learning system is programmed to then determine, based on the correlation, a threshold level of the cell characteristic the patient should exhibit in order to recommend administering the treatment.
摘要翻译：本发明的主题提供了其中识别诊断测试的装置，系统和方法，其中诊断测试用于基于患者细胞的一个或多个特征来确定特定治疗是否对特定患者有效。当开发具有治疗一种或多种疾病潜力的治疗时，该药物可以对与疾病相关的不同细胞系具有不同的作用。机器学习系统被编程为推断出许多不同可测量的细胞特征中的可测量细胞特征，其与不同细胞系对治疗的敏感性数据具有期望的相关性。机器学习系统被编程为然后基于相关性来确定患者应该展示的细胞特征的阈值水平以推荐施用治疗。

10. 发明申请

WO2019108695A1 DETECTING INTRATUMOR HETEROGENEITY OF MOLECULAR SUBTYPES IN PATHOLOGY SLIDE IMAGES USING DEEP-LEARNING 审中-公开
公开(公告)号：WO2019108695A1
公开(公告)日：2019-06-06
申请号：PCT/US2018/062911
申请日：2018-11-28
申请人： NANTOMICS, LLC
发明人： JABER, Mustafa , SONG, Bing , SZETO, Christopher , VASKE, Charles
IPC分类号： G16H50/20 , G16H50/70 , A61B10/00 , G16H10/40
摘要： Techniques are provided for determining molecular subtype classifications based on pathology slide images (SIs). A plurality of training SIs is segmented into a plurality of scaled patches. Each scaled patch is converted into a multiscale descriptor using a deep-learning neural network by mapping each of one or more patch representations to a patch-level descriptor and combining the patch-level descriptors. A classifier model is configured and trained to process the multiscale descriptors such that, for each training SI, the classifier model is operable to assign a patch-level molecular subtype classification to each of the scaled patches corresponding to the training SI and determine a Si-level molecular subtype classification based on the patch-level molecular subtype classifications. A molecular subtyping engine is configured to use the trained classifier model to determine a SI-level molecular subtype classification for a test SI.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式