MACHINE-LEARNING PROCESSING OF AGGREGATE DATA INCLUDING RECORD-SIZE DATA TO PREDICT FAILURE PROBABILITY

    公开(公告)号:US20230087336A1

    公开(公告)日:2023-03-23

    申请号:US17933991

    申请日:2022-09-21

    IPC分类号: G06F11/07 G06N7/00

    摘要: Machine-learning processing of aggregate data including record-size data to predict failure probability is described herein. In an example, a system identifies electronic data that is longitudinal and includes a set of electronic records pertaining to a given subject or to a given object. The system generates a record-size metric that characterizes a size of the electronic data and determines a physical attribute of the given subject or the given object. The system generates a physical-attribute metric based on the physical attribute, generates an input data set that includes the record-size metric and the physical-attribute metric, and generates a failure probability across a given time period and for the given subject or the given object by processing the input data set using a trained machine-learning model. The system determines that an alert condition is satisfied based on the failure probability and outputs an alert representing the failure probability.

    SARS-CoV-2 EPITOPE-TARGETED PEPTIDE IMMUNOSTIMULANTS

    公开(公告)号:US20210380640A1

    公开(公告)日:2021-12-09

    申请号:US17336276

    申请日:2021-06-01

    摘要: Disclosed are compounds, compositions, and methods relating to epitope-targeted immunostimulants (EPIs), which comprise a synthetic peptide ligand and an antibody-recruiting moiety. The peptide ligand binds an epitope on a target and the antibody-recruiting moiety recruits antibodies to the target when the EPI is bound to the epitope on the target. Also disclosed are compositions comprising any of the disclosed EPIs. Also disclosed are methods of stimulating an immune reaction to a microorganism or other pathogen in a subject where an EPI is administered to the subject. Also disclosed are methods of identifying the peptide ligand by using multi-omic analysis.

    MARKERS FOR AMYOTROPHIC LATERAL SCLEROSIS (ALS) AND PRESYMPTOMATIC ALZHEIMER'S DISEASE (PSAD)
    7.
    发明申请
    MARKERS FOR AMYOTROPHIC LATERAL SCLEROSIS (ALS) AND PRESYMPTOMATIC ALZHEIMER'S DISEASE (PSAD) 审中-公开
    脑血管病(ALS)和丙型肝炎病毒(PSAD)的标志物

    公开(公告)号:US20160265057A1

    公开(公告)日:2016-09-15

    申请号:US15025207

    申请日:2014-09-25

    IPC分类号: C12Q1/68

    摘要: Methods to detect amyotrophic lateral sclerosis (ALS) or presymptomatic Alzheimer's disease (PSAD) using an indicator cell assay platform (iCAP) in a test subject are described. Specifically, the disclosure provides a method comprising contacting a biological fluid of said test subject with indicator cells and assessing said indicator cells for the level of expression of an exon of CKIgamma2 that encodes the C-terminal palmitoylated region of said CKIgamma2, to determine the probability that a test subject is afflicted with amyotrophic lateral sclerosis (ALS). Further disclosed are methods of using indicator cells that are pan neuronal populations of glutamatergic (and/or GABAergic) neurons to determine the probability of the presence of presymptomatic or symptomatic Alzheimer's disease (PSAD) in a test subject.

    摘要翻译: 描述了在测试对象中使用指示剂细胞测定平台(iCAP)检测肌萎缩性侧索硬化(ALS)或症状性早老性痴呆(PSAD)的方法。 具体地,本公开提供了一种方法,包括使所述测试对象的生物流体与指示细胞接触,并评估所述指示细胞的CKIgamma2的外显子的表达水平,所述外显子编码所述CKIgamma2的C末端棕榈酰化区域,以确定概率 测试受试者患有肌萎缩性侧索硬化(ALS)。 进一步公开的是使用谷氨酸能(和/或GABA能)神经元的泛神经元群体的指示细胞来确定测试受试者中出现症状性症状或症状性阿尔茨海默氏病(PSAD)的可能性的方法。

    Methods for identifying and monitoring drug side effects
    9.
    发明授权
    Methods for identifying and monitoring drug side effects 有权
    鉴别和监测药物副作用的方法

    公开(公告)号:US09103834B2

    公开(公告)日:2015-08-11

    申请号:US14100301

    申请日:2013-12-09

    摘要: The present invention relates generally to methods for identifying drug side effects by detecting perturbations in organ-specific molecular blood fingerprints. The invention further relates to methods for identifying drug-specific organ-specific molecular blood fingerprints. As such, the present invention provides compositions comprising organ-specific proteins, detection reagents for detecting such proteins, and panels and arrays for determining organ-specific molecular blood fingerprints.

    摘要翻译: 本发明一般涉及通过检测器官特异性分子血液指纹图中的扰动来鉴定药物副作用的方法。 本发明还涉及用于鉴定药物特异性器官特异性分子血液指纹图谱的方法。 因此,本发明提供了包含器官特异性蛋白质的组合物,用于检测这些蛋白质的检测试剂,以及用于测定器官特异性分子血液指纹图谱和阵列。