Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules

Khondoker, Mizanur R., Bachmann, Till T., Mewissen, Muries, Dickinson, Paul, Dobrzelecki, Bartosz, Campbell, Colin J., Mount, Andrews R., Walton, Anthony J., Crain, Jason, Schulze, Holger, Giraud, Gerard, Ross, Alan J., Ciani, Ilenia, Ember, Stuart W. J., Tlili, Chaker, Terry, Jonathan G., Grant, Eilidh, McDonnell, Nicola and Ghazal, Peter 2010. Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules. Journal of Bioinformatics and Computational Biology 08 (06) , 945. 10.1142/S0219720010005063

Full text not available from this repository.

Abstract

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
ISSN: 0219-7200
Last Modified: 28 Jan 2020 03:55
URI: http://orca-mwe.cf.ac.uk/id/eprint/112171

Citation Data

Cited 9 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item