Name
Title | ||
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Identifying common driver modules by equilibrating coverage and mutual exclusivity across pan-cancer data |
Abstract | ||
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It is of significance to identify common driver modules from pan-cancer data to interpret heterogeneity of cancer, and the accumulated omic data have made it into reality. In this paper, the pan-cancer common driver module identification problem is formulated, which takes the frequency difference among various cancers into account. For solving this problem, a K-nearest neighbors based imputation algorithm KNNImp is firstly devised to infer the variation values for some potential significant missing genes. Secondly, a harmonic mean of coverage and mutual exclusivity based random walk algorithm HMCEwalk is proposed. It weights the integrated PPI network with the harmonic mean of gene coverage scores and mutual exclusion scores among various cancer types, and extracts modules through a random walk process. Experiments were implemented on both simulated data and real biological data. The experimental results on simulated data indicate that given two types of cancers, the HMCEwalk algorithm has a stronger tendency to identify a set of modules which not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples for each cancer. The experimental results on biological data show that the presented imputation algorithm does play roles in regaining some important cancer related genes. In comparison with two state-of-the-art identification methods MEXCOwalk and DriveWays, the presented one exhibits competitive performance in most instances in terms of revealing the known cancer genes, producing modules having satisfied coverage and mutual exclusivity for each cancer. Many detected modules engage in the known cancer-related biological pathways. In addition, the presented method does recognize many cancer-associated genes omitted by methods MEXCOwalk and DriveWays. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2022.04.050 | Neurocomputing |
Keywords | DocType | Volume |
00-01,99-00 | Journal | 492 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 3 | |