Abstract
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.
Highlights
Identifying 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
Tppp Vdac1 Prdx2 gag Ywhaz Uchl1 Alb Acaa1a Pebp1 Hba-a2 Hspa5 Cap2. We address this problem by proposing a new statistics called Early Response Index (ERI) that is sensitive to changes of feature expression at early stages
As our experimental data have shown, hundreds of proteins will have high abundance changes after this time point, and identifying biomarkers that have responded to the disease on day 5 will offer opportunities for disease intervention before a large number of proteins are affected
Summary
Identifying 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. Identifying disease correlated molecules at early stages, before the disease process induces high abundance changes in large number of molecules, is a challenging but important problem. Before the disease process induces significant changes, most disease correlated biomarkers only have small abundance changes. Several contemporary data mining algorithms are reviewed in [1,2,3] which include multivariate analysis [4], CART analysis [5], voting panel approach [6], artificial neural
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