A survey of computational tools for downstream analysis of proteomic and other omic datasets

Human Genomics

Lawrence E. Hunter¹  L. Elaine Epperson²  Anis Karimpour-Fard¹ 

[1] Department of Pharmacology, University of Colorado School of Medicine, Aurora 80045, CO, USA;Integrated Center for Genes, Environment, and Health, National Jewish Health, Denver 80206, CO, USA

关键词: Proteomics repository;    SVM;    PCA;    PLS;    Random forests;    Machine learning;    Proteomics;   

Others  :  1232636
DOI  :  10.1186/s40246-015-0050-2

 received in 2015-07-28, accepted in 2015-10-06,  发布年份 2015

【 摘 要 】

Proteomics is an expanding area of research into biological systems with significance for biomedical and therapeutic applications ranging from understanding the molecular basis of diseases to testing new treatments, studying the toxicity of drugs, or biotechnological improvements in agriculture. Progress in proteomic technologies and growing interest has resulted in rapid accumulation of proteomic data, and consequently, a great number of tools have become available. In this paper, we review the well-known and ready-to-use tools for classification, clustering and validation, interpretation, and generation of biological information from experimental data. We suggest some rules of thumb for the reader on choosing the best suitable learning method for a particular dataset and conclude with pathway and functional analysis and then provide information about submitting final results to a repository.

【 授权许可】

   
2015 Karimpour-Fard et al.

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