A survey of computational tools for downstream analysis of proteomic and other omic datasets. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Karimpour-Fard, Anis; Epperson, L.; Hunter, Lawrence
• Is Part Of: Human genomics. Volume 9:Issue 1(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: Proteomics -- Machine learning -- Random forests -- PLS -- PCA -- SVM -- Proteomics repository; Genomics -- Periodicals; Human genome -- Periodicals; Genetic Research -- Periodicals; Pharmacogenetics -- Periodicals; 611.01816
• DOI: 10.1186/s40246-015-0050-2
• Languages: English
• ISSNs: 1479-7364

Abstract

Abstract 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.