Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer
| Autor: | Abdullah Al Mamun, Tasmia Aqila, Ananda Mohan Mondal, Mona Maharjan, Raihanul Bari Tanvir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
clique
Pearson correlation coefficient (PCC) Information Systems and Management Gene regulatory network Cancer Computational biology Clique (graph theory) Biology gene co-expression network medicine.disease Pearson product-moment correlation coefficient lcsh:Z Computer Science Applications lcsh:Bibliography. Library science. Information resources symbols.namesake bipartite graph medicine symbols Bipartite graph Gene co-expression network Biomarker (medicine) network biomarker Gene Information Systems |
| Zdroj: | Data Volume 4 Issue 2 Data, Vol 4, Iss 2, p 81 (2019) |
| ISSN: | 2306-5729 |
| DOI: | 10.3390/data4020081 |
| Popis: | Two graph theoretic concepts&mdash clique and bipartite graphs&mdash are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers&mdash breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)&mdash are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient &ge 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed&mdash maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer. |
| Databáze: | OpenAIRE |
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