Document Detail

Statistically invalid classification of high throughput gene expression data.
MedLine Citation:
PMID:  23346359     Owner:  NLM     Status:  In-Data-Review    
Classification analysis based on high throughput data is a common feature in neuroscience and other fields of science, with a rapidly increasing impact on both basic biology and disease-related studies. The outcome of such classifications often serves to delineate novel biochemical mechanisms in health and disease states, identify new targets for therapeutic interference, and develop innovative diagnostic approaches. Given the importance of this type of studies, we screened 111 recently-published high-impact manuscripts involving classification analysis of gene expression, and found that 58 of them (53%) based their conclusions on a statistically invalid method which can lead to bias in a statistical sense (lower true classification accuracy then the reported classification accuracy). In this report we characterize the potential methodological error and its scope, investigate how it is influenced by different experimental parameters, and describe statistically valid methods for avoiding such classification mistakes.
Shahar Barbash; Hermona Soreq
Related Documents :
24327299 - What do the data show? knowledge map development for comprehensive environmental assess...
22420019 - Exposure rate constants and lead shielding values for over 1,100 radionuclides.
24449019 - Body size and activity times mediate mammalian responses to climate change.
2338639 - Modeling the uniaxial compaction of pharmaceutical powders using the mechanical propert...
23802459 - Novel estimates of aedes aegypti (diptera: culicidae) population size and adult surviva...
24662529 - Using multi-level data to estimate the effect of an 'alcogenic' environment on hazardou...
19717359 - Analysis and modeling of naturalness in handwritten characters.
24245629 - Optimal isolation strategies of emerging infectious diseases with limited resources.
23499499 - The utility of two prognostic models for predicting time to first treatment in early ch...
Publication Detail:
Type:  Journal Article     Date:  2013-01-22
Journal Detail:
Title:  Scientific reports     Volume:  3     ISSN:  2045-2322     ISO Abbreviation:  Sci Rep     Publication Date:  2013  
Date Detail:
Created Date:  2013-01-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101563288     Medline TA:  Sci Rep     Country:  England    
Other Details:
Languages:  eng     Pagination:  1102     Citation Subset:  IM    
The Edmond & Lily Safra Center for Brain Sciences and the Department of Biological Chemistry at the Hebrew University of Jerusalem.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Radial arrangement of Janus-like setae permits friction control in spiders.
Next Document:  Feedback mechanism in depolarization-induced sustained activation of extracellular signal-regulated ...