Name
Abstract | ||
|---|---|---|
Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compbiolchem.2019.03.019 | Computational Biology and Chemistry |
Keywords | Field | DocType |
Driver pathway,Multi-omics data,Cancer,Parthenogenetic algorithm,Model | Data set,Gene,Precision medicine,Copy-number variation,Expression (mathematics),Biology,Correlation,Computational biology,Genetics,Cancer,Personalized medicine | Journal |
Volume | ISSN | Citations |
80 | 1476-9271 | 1 |
PageRank | References | Authors |
0.41 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingli Wu | 1 | 3 | 3.15 |
| Qirong Cai | 2 | 2 | 1.83 |
| Jinyan Wang | 3 | 7 | 2.90 |
| Yuanxiu Liao | 4 | 1 | 0.75 |