Utility-aware screening with clique-oriented prioritization

Journal of Chemical Information and Modeling

Volumn 52, Issue 1, 2012, Pages 29-37

  Swamidass, S Joshua ^a,b   Calhoun, Bradley T ^a   Bittker, Joshua A ^b   Bodycombe, Nicole E ^b   Clemons, Paul A ^b  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ONTOLOGY;

ACCURATE PREDICTION; CLIQUE DISCOVERIES; ECONOMIC FRAMEWORK; HIT RATE; ONTOLOGY-BASED; PATTERN IDENTIFICATION; PRIORITIZATION; SIMILARITY-BASED METHODS;

SCAFFOLDS;

CAENORHABDITIS ELEGANS PROTEIN; HISTONE DEMETHYLASE; KDM4A PROTEIN, HUMAN; MEX 5 PROTEIN, C ELEGANS; MEX-5 PROTEIN, C ELEGANS; NONHISTONE PROTEIN;

ALGORITHM; ANIMAL; ARTICLE; CAENORHABDITIS ELEGANS; CHEMICAL STRUCTURE; CHEMISTRY; DRUG ANTAGONISM; DRUG DEVELOPMENT; HIGH THROUGHPUT SCREENING; HUMAN; METHODOLOGY; MOLECULAR LIBRARY;

ALGORITHMS; ANIMALS; CAENORHABDITIS ELEGANS; CAENORHABDITIS ELEGANS PROTEINS; CHROMOSOMAL PROTEINS, NON-HISTONE; DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; JUMONJI DOMAIN-CONTAINING HISTONE DEMETHYLASES; MODELS, MOLECULAR; SMALL MOLECULE LIBRARIES;

EID: 84858033861     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci2003285     Document Type: Article

References (33)

1. Storey, J.; Dai, J.; Leek, J. The optimal discovery procedure for large-scale signficance testing, with applications to comparative microarray experiments. Biostatistics 2007, 8, 414.
2. Rocke, D. Design and analysis of experiments with high throughput biological assay data. Semin. Cell Dev. Biol. 2004, 15, 703-713. (Pubitemid 39550250)
3. Makarenkov, V.; Zentilli, P.; Kevorkov, D.; Gagarin, A.; Malo, N.; Nadon, R. An efficient method for the detection and elimination of systematic error in high-throughput screening. Bioinformatics 2007, 23, 1648.
4. Glick, M.; Klon, A.; Acklin, P.; Davies, J. Enrichment of extremely noisy high-throughput screening data using a naive Bayes classfier. J. Biomol. Screening 2004, 9, 32.
5. Zhang, J. H.; Wu, X.; Sills, M. A. Probing the primary screening efficiency by multiple replicate testing: A quantitative analysis of hit confirmation and false screening results of a biochemical assay. J. Biomol. Screening 2005, 10, 695-704. (Pubitemid 41476804)
6. Glick, M.; Jenkins, J.; Nettles, J.; Hitchings, H.; Davies, J. Enrichment of highthroughput screening data with increasing levels of noise using support vectormachines, recursive partitioning, and laplacianmodified naive bayesian classifiers. J. Chem. Inf. Model. 2006, 46, 193-200. (Pubitemid 43282114)
7. Seiler, K.; George, G.; Happ, M.; Bodycombe, N.; Carrinski, H.; Norton, S.; Brudz, S.; Sullivan, J.; Muhlich, J.; Serrano, M.; Ferraiolo, P.; Tolliday, N.; Schreiber, S.; Clemons, P. Chembank: A small-molecule screening and cheminformatics resource database. Nucleic Acids Res. 2008, 36, D351.
8. Posner, B. A.; Xi, H.; Mills, J. E. J. Enhanced hts hit selection via a local hit rate analysis. J. Chem. Inf. Model. 2009, 2202-2210.
9. Swamidass, S.; Bittker, J.; Bodycombe, N.; Ryder, S.; Clemons, P. An economic framework to prioritize confirmatory tests following a highthroughput screen. J. Biomol. Screening 2010, 15, 680-686.
10. Swamidass, S.; Calhoun, B.; Bittker, J.; Bodycombe,N.; Clemons, P. Enhancing the rate of scaffold discovery with diversity-oriented prioritization Bioinformatics 2011, .
11. Good, A.; Oprea, T. Optimization of CAMD techniques 3. Virtual screening enrichment studies: A help or hindrance in tool selection? J. Comput.-Aided Mol. Des. 2008, 22, 169-178.
12. Clark, R.; Webster-Clark, D. Managing bias in ROC curves. J. Comput.-Aided Mol. Des. 2008, 22, 141-146.
13. Varin, T.; Gubler, H.; Parker, C.; Zhang, J.; Raman, P.; Ertl, P.; Schuffenhauer, A. Compound Set Enrichment: A Novel Approach to Analysis of Primary HTS Data. J. Chem. Inf. Model. 2010, 277-279.
14. Yan, S.; Asatryan, H.; Li, J.; Zhou, Y. Novel statistical approach for primary highthroughput screening hit selection. J. Chem. Inf. Model. 2005, 45, 1784-1790. (Pubitemid 41784752)
15. Agrawal, R.; Gollapudi, S.; Halverson, A.; Ieong, S. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining; ACM: New York, 2009.
16. Demidova, E.; Fankhauser, P.; Zhou, X.; Nejdl, W. DivQ: Diversification for keyword search over structured databases. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval; ACM: New York, 2010; Vol. 10.
17. Shemetulskis, N.; Dunbar, J.; Dunbar, B.; Moreland, D.; Humblet, C. Enhancing the diversity of a corporate database using chemical database clustering and analysis. J. Comput.-Aided Mol. Des. 1995, 9, 407-416.
18. Vee, E.; Srivastava, U.; Shanmugasundaram, J.; Bhat, P.; Yahia, S. A. Effcient Computation of Diverse Query Results. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering; IEEE Computer Society: Los Alamitos, CA, 2008.
19. Meinl, T.; Ostermann, C.; Berthold, M. R. Maximum-score diversity selection for early drug discovery. J. Chem. Inf. Model. 2011, 51, 237-247.
20. Lajiness, M.; Maggiora, G.; Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 2004, 47, 4891-4896. (Pubitemid 39238264)
21. Jones, M.; Hamana, N.; Nezu, J.; Shimane, M. A novel family of bromodomain genes. Genomics 2000, 63, 40-5. (Pubitemid 30112237)
22. Lee, J.; Thompson, J.; Botuyan, M.; Mer, G. Distinct binding modes specify the recognition of methylated histones H3K4 and H4K20 by JMJD2A-tudor. Nat. Struct. Mol. Biol. 2007, 15, 109-111.
23. Schuffenhauer, A.; Ertl, P.; Roggo, S.; Wetzel, S.; Koch, M.; Waldmann, H. The scaffold tree-visualization of the scaffold universe by hierarchical scaffold classification. J. Chem. Inf. Model. 2007, 47, 47-58. (Pubitemid 46225559)
24. Clark, A.; Labute, P. Detection and assignment of common scaffolds in project databases of lead molecules. J. Med. Chem. 2008, 52, 469-483.
25. Bemis, G. W.; Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-2893. (Pubitemid 26251026)
26. Dreiseitl, S.; Ohno-Machado, L. Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inf. 2002, 35, 352-359. (Pubitemid 36951935)
27. Baldi, P.; Brunak, S. Bioinformatics: The machine learning approach; The MIT Press: Cambridge, MA, 2001.
28. Benz, R.; Swamidass, S.; Baldi, P. Discovery of power-laws in chemical space. J. Chem. Inf. Model. 2008, 48, 1138-1151.
29. Dančík, V.; Seiler, K.; Young, D.; Schreiber, S.; Clemons, P. Distinct biological network properties between the targets of natural products and disease genes. J. Am. Chem. Soc. 2010, 894-901.
30. Samuelson, P. A note on the purormatics: The mansumer's behaviour. Economica 1938, 5, 61-71.
31. Houthakker, H. Revealed preference and the utility function. Economica 1950, 159-174.
32. Varian, H. R. Revealed preference. In Samuelson economics and the twenty-rst century; Oxford University Press: Cary, NC, 2006.
33. Varian, H. Microeconomic analysis; W.W. Norton & Company: New York, 1992; volume 506.