Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles-Reference-Cited by-同舟云学术

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Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles

Published:2015-05-26 Issue:S5 Volume:16 Page:
ISSN:1471-2164
Container-title:BMC Genomics
language:en
Short-container-title:BMC Genomics

Author:

Aibar Sara,Fontanillo Celia,Droste Conrad,Roson-Burgo Beatriz,Campos-Laborie Francisco J,Hernandez-Rivas Jesus M,De Las Rivas Javier

Publisher

Springer Science and Business Media LLC

Subject

Genetics,Biotechnology

Link

https://link.springer.com/content/pdf/10.1186/1471-2164-16-S5-S3.pdf

Reference30 articles.

1. Culhane AC, Schröder MS, Sultana R, Picard SC, Martinelli EN, Kelly C, Haibe-Kains B, Kapushesky M, St Pierre A-A, Flahive W, Picard KC, Gusenleitner D, Papenhausen G, O'Connor N, Correll M, Quackenbush J: GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Res. 2012, 40 (Database): 1060-1066.

2. Venet D, Dumont JE, Detours V: Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome. PLoS Comput Biol. 2011, 7: 1002240-10.1371/journal.pcbi.1002240.

3. De Ridder D, De Ridder J, Reinders MJT: Pattern recognition in bioinformatics. Brief Bioinform. 2013, 14: 633-647. 10.1093/bib/bbt020.

4. Larranaga P: Machine learning in bioinformatics. Brief Bioinform. 2006, 7: 86-112. 10.1093/bib/bbk007.

5. Cruz J, Wishart D: Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006, 2: 59-77.

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