Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230500006-7.doi: 10.11896/jsjkx.230500006

• Big Data & Data Science • Previous Articles     Next Articles

Cancer Subtype Prediction Based on Similar Network Fusion Algorithm

ZHANG Xiaoxi1, LI Dongxi2   

  1. 1 College of Mathematics,Taiyuan University of Technology,Taiyuan,Shanxi 030060,China
    2 College of Big Data,Taiyuan University of Technology,Taiyuan,Shanxi 030060,China
  • Published:2024-06-06
  • About author:ZHANG Xiaoxi,born in 1997,postgra-duate.Her main research interests include data mining and analysis and so on.
    LI Dongxi,born in 1982,Ph.D,associate professor.His main research interests include data mining and biostatistics.
  • Supported by:
    National Natural Science Foundation of China(11571009),Basic Research Project of Shanxi Province(201901D111086),Key Research and Development Project of Shanxi Province(202102020101004) and Research Support Program of Shanxi Pro-vince for Returned Overseas Students(2022-074).

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Abstract: Mining the interaction relationship between genes from gene expression data and construct gene regulatory network is one of the important research topics in bioinformatics.However,the current popular neural network only considers the interaction and association between genes in its architecture,and does not consider the interaction and association between patients.Therefore,a cancer subtype prediction model based on the fusion algorithm of weighted gene similarity network and sample similarity network,namely WGCSS,is proposed in this paper.In this method,the fusion of feature space and sample space information is realized,and the interaction between genes and samples is considered,and the graph convolutional network is used for prediction.Aggregating information in two spaces will lead to a serious oversmoothing problem.Therefore,a residual layer is introduced in the model to alleviate the oversmoothing problem.This method can make the prediction of cancer subtypes more accurate by aggregating the data information in the two spaces.To verify the generalization performance of the method,datasets of invasive breast carcinoma(BRCA),glioblastoma multiforme(GBM),and LUNG(LUNG) are used for analysis,and the resulting high classification accuracy demonstrates the superiority of the method.Survival analysis is also performed on three types of data sets,and it is proved that the method has significant differences in the survival curves of cancer subtypes in three cancer datasets.

Key words: Weighted gene similarity network, Sample similarity network, Residual graph convolutional network, L1 regular, Cancer subtype prediction

CLC Number: 

  • TP399
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