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    SYSTEMS AND METHODS FOR DECONVOLUTION OF EXPRESSION DATA
    55.
    发明公开
    SYSTEMS AND METHODS FOR DECONVOLUTION OF EXPRESSION DATA 审中-实审

    公开(公告)号：EP4383262A3

    公开(公告)日：2024-08-14

    申请号：EP24161223.3

    申请日：2021-03-12

    申请人： BostonGene Corporation

    发明人： ZAITSEV, Aleksandr ,  CHELUSHKIN, Maksim ,  NUZHDINA, Ekaterina ,  DYIKANOV, Daniiar ,  BAGAEV, Alexander ,  ATAULLAKHANOV, Ravshan ,  CHEREMUSHKIN, Ilya ,  SHPAK, Boris ,  ZYRIN, Vladimir

    IPC分类号： G16B40/20 ,  G16B25/10

    CPC分类号： G16B40/20 ,  G16B25/10

    摘要： Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

    METHOD AND SYSTEM FOR DESIGNING DRUG-LIKE MOLECULES FROM DESIRED GENE EXPRESSION SIGNATURES
    56.
    发明公开
    METHOD AND SYSTEM FOR DESIGNING DRUG-LIKE MOLECULES FROM DESIRED GENE EXPRESSION SIGNATURES 审中-公开

    公开(公告)号：EP4407624A1

    公开(公告)日：2024-07-31

    申请号：EP23207229.8

    申请日：2023-11-01

    申请人： Tata Consultancy Services Limited

    发明人： DAS, DIBYAJYOTI ,  ROY, ARIJIT ,  SRINIVASAN, RAJGOPAL ,  CHAKRABARTY, BROTO

    IPC分类号： G16B25/10 ,  G16B40/20

    CPC分类号： G16B5/00 ,  G16B25/10 ,  G16B40/20

    摘要： Drug induced gene expression provides information covering various aspects of drug discovery and development. Recent advances in accessibility of open-source drug-induced transcriptomic data along with ability of deep learning algorithms to understand hidden patterns have opened opportunity for designing drug molecules based on desired gene expression signatures. Embodiments herein provide method and system for cell specific model where gene expressions are processed via pretrained Simplified Molecular Input Line Entry System (SMILES) variational autoencoder (s-VAE) to produce new molecules. The model is trained with drug and drug induced gene expression data as input. Both pretrained s-VAE and profile variational autoencoder (p-VAE) are trained jointly. During joint training, difference between newly generated molecules and existing drug molecules is calculated as joint loss function composed of binary cross entropy loss and Kullback-Leibler divergence loss. This loss is backpropagated to decoder to learn conditional mapping of molecular space to transcriptomic space in cell-specific manner.