Zhejiang Lab

Jing Ma

Jingsong Li

Yu Wang

Yu Tian

Weiguo Chen

Disclosed is a method and system for identifying a protein domain based on a protein three-dimensional structure image. According to the present application, the protein domain is identified based on a structure similarity, the identification errors and omissions of the protein domain caused by protein multi-sequence alignment errors when sequence consistency is not high can be effectively solved. According to the present application, the point cloud segmentation model based on the dynamic graph convolutional neural network is constructed, and by integrating global structural features and local structural features, segmentation of the protein domain and acquisition of semantic labels of the protein domain can be completed at the same time.

Method and system for identifying protein domain based on protein three-dimensional structure image

Provided is a multi-center synergetic cancer prognosis prediction system based on multi-source migration learning. The system includes a model parameter setting module, a data screening module, and a multi-source migration learning module, wherein the model parameter setting module is responsible for setting cancer prognosis prediction model parameters; the data screening module is arranged at a clinical center, and a management center transmits the set model parameter to each clinical center, such that each clinical center inquires a sample feature and prognosis index data from a local database according to the model parameter, so as to preprocess the data; and the multi-source migration learning module includes a source model training unit, a migration weight calculation unit, and a target model calculation unit.

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Hangzhou, China

Jing Ma