Early response index: a statistic to discover potential early stage disease biomarkers

BMC Bioinformatics

Methodology Article

Thomas G. Forsthuber¹  Itay Raphael¹  Sirajul Salekin²  Jianqiu (Michelle) Zhang²  Mehrab Ghanat Bari³ 

[1] Department of Biology, University of Texas at San Antonio, One UTSA Circle, TX 78207, San Antonio, USA;Department of Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, TX 78207, San Antonio, USA;Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, MN, 55905, Rochester, USA;

关键词: Disease correlated features;    Early stage of disease;    Biomarker discovery;    Feature selection;    Gene/protein expression change;    Multiple Sclerosis;   

DOI  :  10.1186/s12859-017-1712-y

 received in 2016-06-26, accepted in 2017-05-30,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundIdentifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.ResultsWe present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point.ConclusionsERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention.

【 授权许可】

CC BY   
© The Author(s) 2017