基于多组学数据识别癌症驱动通路的模型和算法

doi:10.11896/j.issn.1002-137X.2019.09.047

Abstract

Abstract: This paper proposed improved maximum weight submatrix problem model for identifying driver pathways in cancer by integrating somatic mutations,copy number variations,and gene expressions.The model tries to adjust cove-rage and mutual exclusion with the average weight of genes in a pathway,enhances the coverage of the gene set with large weight and relaxes its mutual exclusion constraint.By introducing a greedy based recombination operator,a parthenogenetic algorithm PGA-MWS was presented to solve the model.Experimental comparisons between PGA-MWS and GA were performed on glioblastoma and ovarian cancer datasets.Experimental results show that,compared with GA algorithm,PGA-MWS algorithm based on the improved model can identify gene sets with high coverage and less mutual exclusion.Many of the identified gene sets are involved in known signaling pathways,and have been confirmed to be closely related to cancer cells.Simultaneously,several potential drive pathways can also be discovered.Therefore,the proposed approach may become a useful complementary one for identifying driver pathways.

Key words: Algorithm, Cancer, Driver pathway, Model, Multi-omics data

CLC Number:

TP301

CAI Qi-rong, WU Jing-li. Model and Algorithm for Identifying Driver Pathways in Cancer by Integrating Multi-omics Data[J].Computer Science, 2019, 46(9): 310-314.

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Model and Algorithm for Identifying Driver Pathways in Cancer by Integrating Multi-omics Data

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