Document Detail

Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores.
MedLine Citation:
PMID:  22689749     Owner:  NLM     Status:  MEDLINE    
MOTIVATION: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut-normalized cut prime (FABS-NC(')), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.
RESULTS: We compare the performance of FABS-NC(') to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC(') also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC(') consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC('): In some cases FABS-NC(') produces over half correctly predicted ranking experiment trials than FABS-SVM.
AVAILABILITY: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.
Dorit S Hochbaum; Chun-Nan Hsu; Yan T Yang
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  28     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-06-12     Completed Date:  2013-01-31     Revised Date:  2013-07-12    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  i106-14     Citation Subset:  IM    
Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, USA.
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MeSH Terms
CHO Cells
Drug Discovery / methods*
Pharmaceutical Preparations / analysis*
Support Vector Machines*
Grant Support
Reg. No./Substance:
0/Pharmaceutical Preparations

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