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dc.titleCan Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?
dc.contributor.authorGoh, Wilson Wen Bin
dc.contributor.authorSng, Judy Chia-Ghee
dc.contributor.authorYee, Jie Yin
dc.contributor.authorSee, Yuen Mei
dc.contributor.authorLee, Tih-Shih
dc.contributor.authorWong, Limsoon
dc.contributor.authorLee, Jimmy
dc.identifier.citationGoh, Wilson Wen Bin, Sng, Judy Chia-Ghee, Yee, Jie Yin, See, Yuen Mei, Lee, Tih-Shih, Wong, Limsoon, Lee, Jimmy (2017-12). Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?. Comput Psychiatr 1 (0) : 168-183. ScholarBank@NUS Repository.
dc.description.abstractThe ultra-high risk (UHR) state was originally conceived to identify individuals at imminent risk of developing psychosis. Although recent studies have suggested that most individuals designated UHR do not, they constitute a distinctive group, exhibiting cognitive and functional impairments alongside multiple psychiatric morbidities. UHR characterization using molecular markers may improve understanding, provide novel insight into pathophysiology, and perhaps improve psychosis prediction reliability. Whole-blood gene expressions from 56 UHR subjects and 28 healthy controls are checked for existence of a consistent gene expression profile (signature) underlying UHR, across a variety of normalization and heterogeneity-removal techniques, including simple log-conversion, quantile normalization, gene fuzzy scoring (GFS), and surrogate variable analysis. During functional analysis, consistent and reproducible identification of important genes depends largely on how data are normalized. Normalization techniques that address sample heterogeneity are superior. The best performer, the unsupervised GFS, produced a strong and concise 12-gene signature, enriched for psychosis-associated genes. Importantly, when applied on random subsets of data, classifiers built with GFS are "meaningful" in the sense that the classifier models built using genes selected after other forms of normalization do not outperform random ones, but GFS-derived classifiers do. Data normalization can present highly disparate interpretations on biological data. Comparative analysis has shown that GFS is efficient at preserving signals while eliminating noise. Using this, we demonstrate confidently that the UHR designation is well correlated with a distinct blood-based gene signature.
dc.publisherUbiquity Press, Ltd.
dc.subjectfeature selection
dc.subjectgene expression
dc.subjectultra-high risk
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.sourcetitleComput Psychiatr
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