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

US20190194320A1 METHODS OF IDENTIFYING EPITOPES 审中-公开
公开(公告)号：US20190194320A1
公开(公告)日：2019-06-27
申请号：US16329538
申请日：2017-09-01
申请人： OBLIQUE THERAPEUTICS AB
发明人： Owe ORWAR , Carolina TRKULJA , Max DAVIDSON
IPC分类号： C07K16/28 , C07K14/705 , G16B35/20 , C07K16/00 , G16B40/10
CPC分类号： C07K16/28 , C07K14/705 , C07K16/005 , C07K2317/34 , C07K2317/77 , G01N33/68 , G16B35/20 , G16B40/10
摘要： The present invention relates to methods of identifying an epitope on a protein that can be bound by an antibody. Methods of the invention typically involve a step of limited or restricted proteolysis of a protein and the identification of sites on the protein that are cut by the protease(s) used. The invention also relates to antibodies which bind to epitopes that have been identified by methods of the invention.

2. 发明申请

US20180074038A1 METABOLOMICS PROFILING OF CENTRAL NERVOUS SYSTEM INJURY 审中-公开
公开(公告)号：US20180074038A1
公开(公告)日：2018-03-15
申请号：US15560056
申请日：2016-03-18
申请人： LONDON HEALTH SCIENCES CENTRE RESEARCH INC.
发明人： Douglas Dale FRASER , Robert BARTHA , Arthur BROWN , Tanya Charyk STEWART , Mark DALEY , Greg A. DEKABAN , Timothy DOHERTY , Lisa FISCHER , Jeff HOLMES , Ravi MENON , J. Kevin SHOEMAKER , Tony RUPAR
IPC分类号： G01N33/487 , G06F19/24
CPC分类号： G01N33/487 , G01N24/08 , G01N33/483 , G01N33/6896 , G01N2570/00 , G01N2800/28 , G01N2800/2871 , G16B40/00 , G16B40/10 , G16B40/20 , G16B40/30 , G16C20/70
摘要： A method of diagnosing central nervous system injuries such as acquired brain injury (ABI) and/or acquired spinal cord injury (ASI), including mild TBI (concussion or blast wave), mild ASI (contusion, stretch or partial cord transection), non-TBI brain injury and/or non-TSI spinal cord injury in a subject (animal or human). The method includes (a) obtaining a biological test sample from the subject, identifying metabolites in the subject's sample using metabolomics thereby obtaining a subject's metabolite matrix and generating a subject's profile using the patient's metabolite matrix; and (b) using multivariate statistical analysis and machine learning to compare the subject's profile with predetermined set of profiles of CNS injuries and a predetermined set of profiles of controls to determine if the subject has a CNS injury.

3. 发明申请

US20150051840A1 Identification Of Microorganisms By Spectrometry And Structured Classification 审中-公开
标题翻译：通过光谱法和结构分类鉴定微生物
公开(公告)号：US20150051840A1
公开(公告)日：2015-02-19
申请号：US14387777
申请日：2013-04-02
申请人： bioMerieux
发明人： Kevin Vervier , Pierre Mahe , Jean-Baptiste Veyrieras
IPC分类号： H01J49/16 , G01N33/50 , G06F19/24
CPC分类号： H01J49/164 , C12Q1/04 , G01N33/50 , G06K9/6282 , G16B40/00 , G16B40/10
摘要： A method of identifying by spectrometry of unknown microorganisms from among a set of reference species, including a first step of supervised learning of a classification model of the reference species, a second step of predicting an unknown microorganism to be identified, including acquiring a spectrum of the unknown microorganism; and applying a prediction model according to said spectrum and to the classification model to infer at least one type of microorganism to which the unknown microorganism belong. The classification model is calculated by a structured multi-class SVM algorithm applied to the nodes of a tree-like hierarchical representation of the reference species in terms of evolution and/or of clinical phenotype and having margin constraints including so-called “loss” functions quantifying a proximity between the tree nodes.
摘要翻译：一种通过在一组参考物种中鉴定未知微生物的方法，包括参考物种分类模型的监督学习的第一步骤，预测待鉴定的未知微生物的第二步骤，包括获得未知微生物; 以及根据所述光谱应用预测模型和分类模型来推断未知微生物所属的至少一种类型的微生物。分类模型通过结构化的多类SVM算法计算，该方法应用于进化和/或临床表型方面的参考物种的树状分层表示的节点，并具有包括所谓的“损失”函数的边界约束量化树节点之间的邻近度。

4. 发明申请

US20190237163A1 METHODS FOR FLOW SPACE QUALITY SCORE PREDICTION BY NEURAL NETWORKS 审中-公开
公开(公告)号：US20190237163A1
公开(公告)日：2019-08-01
申请号：US16245343
申请日：2019-01-11
申请人： LIFE TECHNOLOGIES CORPORATION
发明人： Chao Wang , Eugene INGERMAN
IPC分类号： G16B40/10 , G16B30/00 , G06N3/08
CPC分类号： G16B40/10 , C12Q1/6869 , G06N3/0454 , G06N3/08 , G16B30/00
摘要： An artificial neural network is applied to a plurality of flow predictor features to generate a flow space probability of error for a base call. A base quality value for the base call is determined based on the flow space probability of error. The base call and flow predictor features are based on the flow space signal measurements generated in response to the nucleotide flow to the reaction confinement region. For an array of reaction confinement regions, a plurality of parallel neural networks is applied to produce a probability of error for each reaction confinement region. A given neural network of the parallel neural networks is applied to the plurality of flow predictor features corresponding to a given reaction confinement region in the array to provide the flow space probability of error for the given reaction confinement region.

5. 发明申请

US20190233898A1 Methods and Systems for Determining Proportions of Distinct Cell Subsets 审中-公开
公开(公告)号：US20190233898A1
公开(公告)日：2019-08-01
申请号：US16226294
申请日：2018-12-19
申请人： The Board of Trustees of the Leland Stanford Junior University
发明人： Aaron M. Newman , Arash Ash Alizadeh
IPC分类号： C12Q1/6886 , G16B25/00 , G16B40/10 , G16C20/20 , C12Q1/6881 , G01N33/50 , C12Q1/6809
CPC分类号： C12Q1/6886 , C12Q1/6809 , C12Q1/6881 , C12Q2600/106 , C12Q2600/158 , G01N33/5005 , G16B25/00 , G16B40/10 , G16C20/20 , C12Q2537/165
摘要： Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

6. 发明授权

US10007766B2 Predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods 有权
公开(公告)号：US10007766B2
公开(公告)日：2018-06-26
申请号：US15207825
申请日：2016-07-12
申请人： Biodesix, Inc.
发明人： Joanna Röder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Arni Steingrimsson , Heinrich Röder , Senait Asmellash , Kevin Sayers , Caroline Maher , Jeffrey Weber
IPC分类号： C12N5/00 , C12N5/02 , G06F19/00 , G01N33/574 , G01N33/68 , G06F19/24
CPC分类号： G16H50/20 , G01N33/5743 , G01N33/6851 , G01N2333/70532 , G01N2800/52 , G06F19/00 , G06F19/3456 , G06F19/3481 , G16B40/00 , G16B40/10 , G16B40/20 , G16H10/40 , G16H20/10 , G16H20/30
摘要： A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

7. 发明申请

US20170358434A1 BIG-DATA ANALYZING METHOD AND MASS SPECTROMETRIC SYSTEM USING THE SAME METHOD 审中-公开
公开(公告)号：US20170358434A1
公开(公告)日：2017-12-14
申请号：US15617417
申请日：2017-06-08
申请人： Shimadzu Corporation , Teikyo University
发明人： Masaaki MATSUURA , Yuichiro Fujita , Shigeki Kajihara
IPC分类号： H01J49/00 , G06F17/30
CPC分类号： H01J49/0036 , G01N2800/7028 , G06F16/287 , G16B40/10 , G16C20/20 , G16C20/70
摘要： Provided is a method for sorting a number of samples into an appropriate number of clusters according to their characteristics. Highly-correlated peaks are extracted from mass spectrum data obtained for the samples (S2). Using the extracted peaks, highly-correlated sample pairs are extracted (S3). While removing samples having low degrees of correlation, highly-correlated sample pairs are integrated to form core clusters (S4). Using singular peaks characterizing each core cluster, two or more core clusters are integrated to form clusters (S5-S7). These clusters include mixed clusters in which two or more clusters are mixed. Member determination formulae are created based on the singular peaks of each cluster (S8-S12). All samples, including those which have been excluded from the cluster determination process, are classified into clusters based on the member determination formulae (S14). The member determination formulae can also be used to assign a new sample to one of the cluster.

8. 发明申请

US20190213473A1 PREDICTING QUALITY OF SEQUENCING RESULTS USING DEEP NEURAL NETWORKS 审中-公开
公开(公告)号：US20190213473A1
公开(公告)日：2019-07-11
申请号：US15863790
申请日：2018-01-05
申请人： Illumina, Inc.
发明人： Anindita DUTTA , Amirali Kia
IPC分类号： G06N3/08 , G06N5/02
CPC分类号： G06N3/08 , C12Q1/6869 , G06N5/022 , G16B30/00 , G16B40/10 , C12Q2537/165
摘要： The technology disclosed predicts quality of base calling during an extended optical base calling process. The base calling process includes pre-prediction base calling process cycles and at least two times as many post-prediction base calling process cycles as pre-prediction cycles. A plurality of time series from the pre-prediction base calling process cycles is given as input to a trained convolutional neural network. The convolutional neural network determines from the pre-prediction base calling process cycles, a likely overall base calling quality expected after post-prediction base calling process cycles. When the base calling process includes a sequence of paired reads, the overall base calling quality time series of the first read is also given as an additional input to the convolutional neural network to determine the likely overall base calling quality after post-prediction cycles of the second read.

9. 发明申请

US20190204296A1 NANOPORE SEQUENCING BASE CALLING 审中-公开
公开(公告)号：US20190204296A1
公开(公告)日：2019-07-04
申请号：US16325357
申请日：2017-07-10
申请人： THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
发明人： Marcus STOIBER , James BROWN
IPC分类号： G01N33/487 , C12Q1/6869 , G16B30/00 , G16B40/00 , G16B50/00
CPC分类号： G01N33/48721 , C12Q1/68 , C12Q1/6869 , C12Q2563/116 , C12Q2565/631 , G16B30/00 , G16B40/00 , G16B40/10 , G16B50/00 , C12Q2537/165
摘要： Disclosed herein are systems and methods for nanopore sequencing basecalling. In one embodiment, the method can include: receiving raw nanopore sequencing data comprising a plurality of continuous data acquisition (DAC) values corresponding to a biomolecule; normalizing the raw nanopore sequencing data to generate normalized nanopore sequencing data comprising a plurality of normalized DAC values; generating, using a first neural network (NN) and a normalized DAC value of the plurality of normalized DAC values, a vector of transformed probability values, segmenting the plurality of normalized DAC values into a plurality of discrete events; generating, using a second neural network and the event vector, an element determination of the biomolecule.

10. 发明申请

US20160363581A1 METHOD AND APPARATUS FOR IDENTIFICATION OF BIOMARKERS IN BREATH AND METHODS OF USING SAME FOR PREDICTION OF LUNG CANCER 审中-公开
标题翻译：用于鉴定生物标记物的方法和装置及其使用方法预测肺癌
公开(公告)号：US20160363581A1
公开(公告)日：2016-12-15
申请号：US15177695
申请日：2016-06-09
申请人： Michael PHILLIPS
发明人： Michael PHILLIPS
IPC分类号： G01N33/497 , A61B6/03 , A61B6/00 , H01J49/00
CPC分类号： G01N33/497 , A61B6/032 , A61B6/50 , G01N2033/4975 , G16B20/00 , G16B40/10 , G16B40/20 , G16H50/20 , H01J49/00
摘要： The present invention provides a method for identifying biomarkers and generating an output indicative of lung cancer. The method for identifying biomarkers comprises the steps of collecting a breath sample from subjects known to have lung cancer and subjects known to be free of lung cancer; analyzing the collected breath samples to determine all mass ions in each of the collected breath samples using at least one time-resolved separation technique and at least one mass-resolved separation technique; identifying a subset of the determined mass ions in a processor as the biomarkers for detecting lung cancer, the subset of the determined mass ions are statistically significant for detecting lung cancer; and combining the subset of the determined mass ions in a multivariate algorithm in the processor to generate a value of a discriminant function indicating the likelihood that the subject has lung cancer.
摘要翻译：本发明提供了鉴定生物标志物并产生指示肺癌的输出的方法。用于鉴定生物标志物的方法包括从已知具有肺癌的受试者和已知无肺癌患者收集呼吸样品的步骤; 分析收集的呼吸样品以使用至少一种时间分辨分离技术和至少一种质量分辨分离技术来确定每个采集的呼吸样品中的所有质量离子; 将处理器中确定的质量离子的子集识别为用于检测肺癌的生物标志物，确定的质量离子的子集对于检测肺癌具有统计学意义; 以及将所确定的质量离子的子集合在所述处理器中的多变量算法中，以生成指示所述受试者患有肺癌的可能性的判别函数的值。

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