A Simple Method to Combine Multiple Molecular Biomarkers for Dichotomous Diagnostic Classification

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2006 Oct 13

PMID 17032455

Citations 8

Authors

Manju R Mamtani

Tushar P Thakre

Mrunal Y Kalkonde

Manik A Amin

Yogeshwar V Kalkonde

Amit P Amin

Hemant Kulkarni

Affiliations

Soon will be listed here.

Abstract

Background: In spite of the recognized diagnostic potential of biomarkers, the quest for squelching noise and wringing in information from a given set of biomarkers continues. Here, we suggest a statistical algorithm that--assuming each molecular biomarker to be a diagnostic test--enriches the diagnostic performance of an optimized set of independent biomarkers employing established statistical techniques. We validated the proposed algorithm using several simulation datasets in addition to four publicly available real datasets that compared i) subjects having cancer with those without; ii) subjects with two different cancers; iii) subjects with two different types of one cancer; and iv) subjects with same cancer resulting in differential time to metastasis.

Results: Our algorithm comprises of three steps: estimating the area under the receiver operating characteristic curve for each biomarker, identifying a subset of biomarkers using linear regression and combining the chosen biomarkers using linear discriminant function analysis. Combining these established statistical methods that are available in most statistical packages, we observed that the diagnostic accuracy of our approach was 100%, 99.94%, 96.67% and 93.92% for the real datasets used in the study. These estimates were comparable to or better than the ones previously reported using alternative methods. In a synthetic dataset, we also observed that all the biomarkers chosen by our algorithm were indeed truly differentially expressed.

Conclusion: The proposed algorithm can be used for accurate diagnosis in the setting of dichotomous classification of disease states.

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