A co-expression modules based gene selection for cancer recognition

Journal of Theoretical Biology

Volumn 362, Issue , 2014, Pages 75-82

  Lu, Xinguo ^a,b   Deng, Yong ^a   Huang, Lei ^a   Feng, Bingtao ^a   Liao, Bo ^a  

^a HUNAN UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Cancer recognition; Gene expression data; WGCNA

Indexed keywords

ALGORITHM; CANCER; CLASSIFICATION; CORRELATION; GENE EXPRESSION;

ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; CLASSIFICATION ALGORITHM; CO EXPRESSION MODULES BASED GENE SELECTION; COLON CANCER; DECISION TREE; GENE EXPRESSION; GENE ONTOLOGY; GENETIC ANALYSIS; LARGE CELL LYMPHOMA; SUPPORT VECTOR MACHINE; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGY; BREAST TUMOR; COLON TUMOR; DNA MICROARRAY; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETIC EPIGENESIS; GENETICS; HUMAN; METABOLISM; NEOPLASMS; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; PROTEIN MICROARRAY; REPRODUCIBILITY; SYSTEMS BIOLOGY; THEORETICAL MODEL; TUMOR CELL LINE;

ALGORITHMS; BREAST NEOPLASMS; CELL LINE, TUMOR; COLONIC NEOPLASMS; COMPUTATIONAL BIOLOGY; EPIGENESIS, GENETIC; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; HUMANS; LYMPHOMA, LARGE B-CELL, DIFFUSE; MODELS, THEORETICAL; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; PROTEIN ARRAY ANALYSIS; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINES; SYSTEMS BIOLOGY;

EID: 84921858312     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2014.01.005     Document Type: Article

References (25)

1. Baudino T.A., McKay C., Pendeville-Samain H., et al. c-Myc is essential for vasculo-genesis and angiogenesis during development and tumor progression. Genes Dev. 2002, 16(19):2530-2543.
2. Cho, S.B., Won, H.H., et al., 2003. Machine learning in DNA microarray analysis for cancer classification. In: Proceedings of the First Asia-Pacific Bioinformatics Conference (APBC 2003), pp. 189-198.
3. Chuang H., Lee E., et al. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007, 16(October). 10.1038/msb4100180.
4. Conde L., Mateos A., Herrero J., Dopazo J. Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data. Neural Networks for Signal Processing XII 2002, 77-86. IEEE Press, New York. Boulard, Adali, Bengio, Larsen, Douglas (Eds.).
5. Fontanini G., Boldrini L., Calcinal A., et al. Thrombospondins and messenger RNA expression in breast cancer. relationship with p53 alterations, angio-genic growth factors, and vascular density[J]. Clin. Cancer Res. 1999, 5(3):156.
6. Fuller T.F., Ghazalpour A., Aten J.E., Drake T.A., Lusis A.J., Horvath S. Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm. Genome 2007, 18(6-7):463-472.
7. Golub T.R., Slonim D.K., Tamayo P., et al. Molecular classification of cancer. class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
8. Kuramochi M., Karypis G. Gene classification using expression profiles. a feasibility study. Int. J. Artif. Intell. Tools 2005, 14(4):641-660.
9. Langfelder P., Horvath S. WGCNA. an R package for weighted correlation network analysis. BMC Bioinform. 2008, 9:559.
10. Langfelder, Peter, Horvath, Stev, 2009. Tutorial for the WGCNA Package for R. September 3.
11. Li Y.X., Ruan X.G. Feature selection for cancer classification based on support vector machine. J. Comput. Res. Dev. 2005, 42(10):1796-1801.
12. Liu Z., Wang Y., et al. Detecting and analyzing differentially activated pathways in brain regions of Alzheimer's disease patients. Mol. BioSyst. 2011, 7:1441-1452.
13. Liu F., Sheng S., Wang X., et al. NGX6 in Wnt/-catenin signaling pathway in colon cancer. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2011, 36(3):235-242.
14. Lu, X.G., Lin, Y.P., Yang, X.L., et al., 2006. Using most similarity tree based clustering to select the top most discriminating genes for cancer detection. In: The Proceedings of The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC). Zakopane, Poland, pp. 931-940.
15. Magdinier F., Venezia D., Lenoir G.M., et al. BRCA1 expression during prenatal development of the human mammary gland. Oncogene 1999, 18(27):4039-4043.
16. Parsons L., Haque E., Liu H. Subspace clustering for high dimensional data. a review. SIGKDD Explor. 2004, 6(1):90-105.
17. Rajin, J., Marquis, S.T., Gardner, H.P., et al., 1997. Developmental expression of BR-CA2 colocalizes with BRCA1 and is associated with proliferation and differentiation in multiple tissues. Dev. Biol., 184.
18. Raychaudhur, S., Stuar, J.M., Altma, R.B., 2000. Principal components analysis to summarize microarray experiments: application to sporulation time series. In: Pacific Symposium on Biocomputing Honolulu, Hawaii, pp. 452-463.
19. Robey R.W., Polgar O., Deeken J., et al. ABCG2: determining its relevance in clinical drug resistance. Cancer Metastasis Rev. 2007, 26(1):39-57.
20. Shipp M.A., Ross K.N., Tamayo P., et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med. 2002, 8:68-74.
21. Sun, S., Wang, Y., Chen, L., et al., 2013. Spatio-Temporal Analysis of Type 2 Diabetes Mellitus Based On Differential Expression Networks.
22. Wang X., Gotoh O. A robust gene selection method for microarray-based cancer classification. Cancer Inf. 2010, 9:15-30.
23. Witten I.H., Frank E. Data Mining. Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann. second edition.
24. Xu L., Sgroi D., Sterneer C.J., et al. Mutational analysis of CDKN2 in human breast carcinoma. Cancer Res. 1994, 54:5262.
25. Zhang B., Horvath S. A General Framework for Weighted Gene Co-Expression Network Analysis 2005, Published by The Berkeley Electronic Press.