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    SYSTEMS AND METHODS FOR PREDICTING CLINICAL RESPONSE
    1.
    发明公开
    SYSTEMS AND METHODS FOR PREDICTING CLINICAL RESPONSE 审中-公开

    公开(公告)号：US20240355485A1

    公开(公告)日：2024-10-24

    申请号：US18635663

    申请日：2024-04-15

    Applicant: Tempus AI, Inc.

    Inventor： Mark Carty ,  Raphael Pelossof ,  Talal Ahmed ,  Ameen Salahudeen

    IPC: G16H70/40 ,  G16B5/00 ,  G16B40/20

    CPC classification number: G16H70/40 ,  G16B5/00 ,  G16B40/20

    Abstract: The disclosure provides methods and systems for predicting an effect of a pharmaceutical agent in a test subject of a first species. Information about the test subject is input into a multi-task model comprising a plurality of parameters. The model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs including a predicted effect of the pharmaceutical agent in the test subject and, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification. The information about the test subject includes a plurality of abundance values including, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.

    Technique for Identifying Features
    4.
    发明公开
    Technique for Identifying Features 审中-公开

    公开(公告)号：US20240331800A1

    公开(公告)日：2024-10-03

    申请号：US18732589

    申请日：2024-06-03

    Applicant: ExSano, Inc.

    Inventor： Steven Elliot Stupp

    IPC: G16B20/20 ,  G16B20/00 ,  G16B40/00 ,  G16B40/20 ,  G16B40/30

    CPC classification number: G16B20/20 ,  G16B20/00 ,  G16B40/00 ,  G16B40/20 ,  G16B40/30

    Abstract: During a feature-selection technique, an electronic device calculates combinations of features and noise vectors, where a given combination corresponds to a given feature and a given noise vector. Then, the electronic device determines statistical associations between information specifying types of events and the combinations, where a given statistical association corresponds to the types of events and a given combination. Moreover, the electronic device identifies a noise threshold associated with the combinations. Next, for a group of combinations having statistical associations equal to or greater than the noise threshold, the electronic device selects a subset of the features based at least in part on a first 10 aggregate property of the group of combinations, where the first aggregate property comprises numbers of occurrences of the features in the group of combinations.

    INTERPRETABLE RNA FOUNDATION MODEL FOR RNA STRUCTURE AND FUNCTION PREDICTIONS
    5.
    发明公开
    INTERPRETABLE RNA FOUNDATION MODEL FOR RNA STRUCTURE AND FUNCTION PREDICTIONS 审中-公开

    公开(公告)号：US20240331798A1

    公开(公告)日：2024-10-03

    申请号：US18619010

    申请日：2024-03-27

    Applicant: The Chinese University of Hong Kong

    Inventor： Yu LI ,  Jiayang CHEN

    IPC: G16B15/10 ,  G16B40/20 ,  G16B40/30

    CPC classification number: G16B15/10 ,  G16B40/20 ,  G16B40/30

    Abstract: A foundation model for analysis of RNA sequences, including ncRNA sequences, can be trained to provide output embeddings (in a high-dimensional space) corresponding to input RNA sequences. Training of the RNA foundation model can use a large-scale dataset of RNA sequences without any annotation as to structure or function. The trained RNA foundation model can thereafter be used to produce embeddings that can be used as input features in downstream task-specific machine-learning models (or other computer models) that can learn to predict particular aspects of structure and/or function for a given RNA sequence.

    EXTRACTING PROPERTIES FROM A SPARSE DATA SET BY APPLYING HYPERDIMENSIONAL COMPUTING AND DIMENSION REDUCTION
    6.
    发明公开
    EXTRACTING PROPERTIES FROM A SPARSE DATA SET BY APPLYING HYPERDIMENSIONAL COMPUTING AND DIMENSION REDUCTION 审中-公开

    公开(公告)号：US20240321397A1

    公开(公告)日：2024-09-26

    申请号：US18612240

    申请日：2024-03-21

    Applicant: Accenture Global Solutions Limited

    Inventor： Maziyar Baran Pouyan

    IPC: G16B30/00 ,  G16B40/20 ,  G16H10/40

    CPC classification number: G16B30/00 ,  G16B40/20 ,  G16H10/40

    Abstract: The present disclosure relates to a system, computer readable medium, and method for applying hyperdimensional computing and dimension reduction to extract properties from a sparse data set. Applying hyperdimensional computing can solve issues of dimensionality and dropout causing sparse data by expanding the dimension of the data. The result of hyperdimensional computing can involve too much data to be reasonably suitable for downstream computing processes (e.g., clustering for classification). Transforming the hyperdimensional embeddings provided by hyperdimensional computing into simplified/reduced embeddings can solve the problems of processing extremely large data. This improvement in accuracy and usefulness/useability of the sparse data helps reduce the need for extensive time, computing resources, and expensive equipment to extract expression data from deeper from cells.

    WHITE BLOOD CELL CONTAMINATION DETECTION
    7.
    发明公开
    WHITE BLOOD CELL CONTAMINATION DETECTION 审中-公开

    公开(公告)号：US20240312564A1

    公开(公告)日：2024-09-19

    申请号：US18604046

    申请日：2024-03-13

    Applicant: GRAIL, LLC

    Inventor： Sahar Nohzadeh-Malakshah ,  Qinwen Liu ,  Seyedmehdi Shojaee ,  Jing Xiang ,  Joseph Marcus ,  Chenlu Hou ,  John F. Beausang ,  Jonathan Heiss ,  Oliver Claude Venn

    IPC: G16B30/00 ,  C12Q1/6869 ,  G16B40/20

    CPC classification number: G16B30/00 ,  C12Q1/6869 ,  G16B40/20 ,  C12Q2600/154

    Abstract: Methods for WBC contamination detection are disclosed. The computer-implemented methods for WBC contamination detection aim to assess whether a sample is contaminated by the WBC-shed DNA and may further determine a level of contamination. A first coverage-based approach assesses normalized coverage of sequence reads of a test sample at each genomic locus in a feature set of genomic loci. A contamination metric may be calculated based on a distance of the test sample's normalized coverage to a distribution of purified cfDNA samples. A second methylation-based approach deconvolves tissue type based on methylation features. A distribution is generated based on tissue type fractions of purified cfDNA samples from non-cancer subjects. The contamination metric is calculated based on a distance relative to the distribution of tissue type fractions. A third quantitative coverage-based approach generates distributions of coverage for cfDNA samples and for WBC samples for each genomic locus. A contamination metric is calculated as a fractional contribution of WBC-shed DNA that maximizes a likelihood based on the distributions of coverage.

    METHOD FOR IDENTIFYING A CANDIDATE, NAMELY A GENE LOCATION AND/OR A SEQUENCE VARIANT, INDICATIVE FOR AT LEAST ONE (PHENOTYPIC) TRAIT
    9.
    发明公开
    METHOD FOR IDENTIFYING A CANDIDATE, NAMELY A GENE LOCATION AND/OR A SEQUENCE VARIANT, INDICATIVE FOR AT LEAST ONE (PHENOTYPIC) TRAIT 审中-公开

    公开(公告)号：US20240304278A1

    公开(公告)日：2024-09-12

    申请号：US18600357

    申请日：2024-03-08

    Applicant: KWS SAAT SE & Co. KGaA

    Inventor： Bjoern Oest HANSEN

    IPC: G16B20/00 ,  G16B40/20

    CPC classification number: G16B20/00 ,  G16B40/20

    Abstract: The present application is directed to a method for identifying at least one candidate (Loc), namely a gene location and/or a sequence variant, indicative for at least one selected (phenotypic) trait of an organism, in particular of a plant, comprising the steps of:



    a. receiving a plurality of candidate lists (Can1, Can2, Can3) of candidates (Loc), the candidate lists being ordered;
    b. receiving a reference set (RefDB) with gene locations and/or sequence variants;
    c. matching at least a subset of candidates (Loc) from the candidate lists (Can1, Can2, Can3) with the reference list (RefDB) to determine an evaluation value (EV) for at least the subset;
    d. assigning each evaluation value (EV) to the respective candidate (Loc) in the respective candidate lists (Can1, Can2, Can3);
    e. calculating for each candidate list a performance value based on the evaluation value (EV), in particular by using the evaluation values (EV);
    f. selecting at least one candidate (Loc) as (preferred) candidate (Loc) from one of the candidate lists (Can1, Can2, Can3) using the performance values.

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