公开(公告)号：US20230053405A1

公开(公告)日：2023-02-23

申请号：US17891750

申请日：2022-08-19

Applicant: DASSAULT SYSTEMES

Inventor： Arthur BALL ,  Nicolas PECUCHET

IPC: G16B40/00 ,  G16B30/10 ,  G06N3/02

Abstract: A computer-implemented method for machine-learning a neural network for variant calling with respect to a reference genome. The neural network takes as input one or more sets of data pieces each specifying a respective read aligned relative to a genomic position of the reference genome. The neural network outputs information with respect to presence of a variant at the genomic position. The neural network includes, for each set of data pieces, a respective function configured to take as input and process the set of data pieces. The respective function is symmetric. The machine-learning method improves variant calling with respect to a reference genome.

公开(公告)号：US20200027564A1

公开(公告)日：2020-01-23

申请号：US16504158

申请日：2019-07-05

Applicant: DASSAULT SYSTEMES

Inventor： Guillaume LEFEBVRE ,  Arthur BALL ,  Nicolas PECUCHET ,  Marine ZULIAN

IPC: G16H50/50 ,  G16B5/20

Abstract: The disclosure notably relates to a computer-implemented method for simulating evolution of a tumor associated to an oncogene. The method includes providing a plurality of pieces of data, each corresponding to a given cell of the tumor, and includes a degree of activation of the oncogene in the given cell. The method further includes providing a model configured to take an input piece of data and to output information on proliferation of the respective given cell corresponding to the input piece of data. The information on proliferation depends on the degree of activation of the oncogene. The method further includes running the model on one or more pieces of data of the plurality of pieces of data and updating the plurality of pieces of data based on the result of the running. Such a method improves the simulation of the evolution of a tumor.

公开(公告)号：US20230197194A1

公开(公告)日：2023-06-22

申请号：US18086279

申请日：2022-12-21

Applicant: DASSAULT SYSTEMES

Inventor： Pauline SÉCHET ,  Arthur BALL

IPC: G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G06N3/08

CPC classification number: G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G06N3/08

Abstract: A computer-implemented method for training a neural network for inferring a gene expression profile. The method includes obtaining a matrix of potential regulations between genes of a set of genes of a sequence of reference genome, obtaining a neural network having an input layer of nodes and an output layer of nodes, the input layer and the output layer having an equivalent node for representing each gene of the set of genes of the sequence of the reference genome, each node of the input layer representing a regulator gene and each node of the output layer representing a regulated gene, adding connections to the neural network from the nodes of the input layer to the nodes of the output layer, the added connections being extracted from the obtained matrix of potential regulations, training the neural network by using a set of gene expression profiles of the observed biological process, each connection of the trained the neural network being weighted, and removing connections of the trained neural network having an insignificant weight value.