How to Improve Postgenomic Knowledge Discovery Using Imputation. (11th January 2009)

• Record Type: Journal Article
• Authors: Sehgal, Muhammad Shoaib B.; Gondal, Iqbal; Dooley, Laurence S.; Coppel, Ross
• Other Names: Serpedin Erchin Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2009(2009)
• Publication Date: 2009-01-11
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2009/717136
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures.