ArticleInterdisciplinary sciences, computational life sciences2022
Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes.
Article in Interdisciplinary sciences, computational life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Internal validation of a residual thyroid recurrence model after PTC.Frontiers in oncology · 2026Article
- Unlocking TB-COPD Pathogenesis: A Biotechnological Approach Targeting the PI3K/Akt Pathway.Iranian journal of biotechnology · 2025Article
- Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters.Plants (Basel, Switzerland) · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.Biomedical optics express · 2023Article
- Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics.PloS one · 2023Article
- A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.