Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes-Reference-Cited by-同舟云学术

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Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes

Published:2021-10-25 Issue: Volume: Page:
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Cao Dan,Xu Na,Chen Yuan,Zhang Hongyan,Li Yuting,Yuan Zheming

Funder

Natural Science Foundation of Jilin Province

Scientific Research Foundation of Education Office of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-021-00485-w.pdf

Reference64 articles.

1. Galton D, Ferns G (1999) Genetic markers to predict polygenic disease:a new problem for social genetics. QJM 92(4):223–232. https://doi.org/10.1093/qjmed/92.4.223

2. Carpenter AE, Sabatini DM (2004) Systematic genome-wide screens of gene function. Nat Rev Genet 5(1):11–22. https://doi.org/10.1038/nrg1248

3. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4(1):1–45. https://doi.org/10.2202/1544-6115.1128

4. Yin L, Cai Z, Zhu B et al (2018) Identification of key pathways and genes in the dynamic progression of HCC based on WGCNA. Genes 9(2):92. https://doi.org/10.3390/genes9020092

5. Voineagu I, Wang X, Johnston P et al (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474(7351):380–384. https://doi.org/10.1038/nature10110

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