NDLI: A multi-objective program for quantitative subtyping of clinically relevant phenotypes

Content Provider	IEEE Xplore Digital Library
Author	Jiangwen Sun Jinbo Bi Kranzler, H.R.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA (Jiangwen Sun; Jinbo Bi) \|\| Treatment Research Center, University of Pennsylvania, Philadelphia, PA, USA (Kranzler, H.R.)
Abstract	Identifying genetic variations that underlie human disease is very important to advance our understanding of the disease's pathophysiology and promote its personalized treatment. However, many disease phenotypes have complex clinical manifestations and a complicated etiology. Gene finding efforts for complex diseases have had limited success to date. Research results suggest that one way to enhance these efforts is to differentiate subtypes of a complex multifactorial disease phenotype. Existing subtyping methods rely on cluster analysis using only clinical features of a disorder without guidance from genetic data, resulting in subtypes for which genotype association may be limited. In this work, we seek to derive a novel computational method based on multi-objective programming that is capable of clinically categorizing a disease phenotype so as to discover genetically different subtypes. Our approach optimizes two objectives: (1) the cluster-derived subtypes should differ significantly on clinical features; (2) these subtypes can be well separated using candidate genes. This work has been motivated by clinical studies of opioid dependence, a serious, prevalent disorder that is heterogeneous phenotypically. Analyses on a sample of 1,470 European American subjects aggregated from multiple genetic studies of opioid dependence show that the proposed algorithm is superior to existing subtyping methods.
Starting Page	1
Ending Page	6
File Size	700743
Page Count	6
File Format	PDF
ISBN	9781467325592
e-ISBN	9781467325608
e-ISBN	9781467325585
DOI	10.1109/BIBM.2012.6392679
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Measurement Cluster analysis Simulated annealing Gene finding Opioid dependence Genetics Multi-objective optimization Subtyping Diseases
Content Type	Text
Resource Type	Article

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