How to improve postgenomic knowledge discovery using imputation
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
- Date: 2009
- Type: Text , Journal article
- Relation: Eurasip Journal on Bioinformatics and Systems Biology Vol. 2009, no. 1 (2009), p. 1-14
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- Description: 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
Heuristic non parametric collateral missing value imputation : A step towards robust post-genomic knowledge discovery
- Authors: Sehgal, Muhammad Shoaib B , Gondal, Iqbal , Dooley, Laurence , Coppel, Ross
- Date: 2008
- Type: Text , Conference paper
- Relation: Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008) Vol. 5625
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- Description: Microarrays are able to measure the patterns of expression of thousands of genes in a genometo give profiles that faciliate much faster analysis of biological process for diagnosis, prognosis and tailored drug discovery. Microarrays, however commonly have missing values, various algorithms have been proposed including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute). Local Least Square Impute (LLSImpute) and K-Nearest Neighbour (KNN).