Fast rule-based bioactivity prediction using associative classification mining

Journal of Cheminformatics

Volumn 4, Issue 11, 2012, Pages

  Yu, Pulan ^a   Wild, David J ^a  

^a Indiana University   (United States)

Author keywords

Associative classification mining; Bayesian; Fingerprint; Pipeline Pilot; SVM

Indexed keywords

EID: 84872194892     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-4-29     Document Type: Article

References (61)

1. Tong W, Hong H, Fang H, Xie Q, Perkins R: Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models. J Chem Inf Comput Sci 2003, 43:525
2. Prathipati P, Ma NL, Keller TH: Global Bayesian Models for the Prioritization of Antitubercular Agents. J Chem Inf Model 2008, 48:2362-2370. 3. Vogt M, Godden JW, Bajorath J: Bayesian Interpretation of a Distance
3. Bayesian interpretation of a distance Function for Navigating High-Dimensional Descriptor Spaces. J Chem Inf Model 2007, 47:39-46 (Pubitemid 46225558)
4. Nigsch F, Bender A, Jenkins JL, Mitchell JBO: Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics. J Chem Inf Model 2008, 48:2313-2325
5. Schwaighofer A, Schroeter T, Mika S, Hansen K, ter Laak A, Lienau P, Reichel A, Heinrich N, Muller K-R: A Probabilistic Approach to Classifying Metabolic Stability. J Chem Inf Model 2008, 48:785-796 (Pubitemid 351760834)
6. Liew CY, Ma XH, Liu X, Yap CW: SVM Model for Virtual Screening of Lck Inhibitors. J Chem Inf Model 2009, 49:877-885
7. Mohr JA, Jain BJ, Obermayer K: Molecule Kernels: A Descriptor- and Alignment-Free Quantitative Structure-Activity Relationship Approach. J Chem Inf Model 2008, 48:1868-1881
8. Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, Low BC, Chen YZ: Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds. J Chem Inf Model 2008, 48:1227-1237
9. Sommer S, Kramer S: Three Data Mining Techniques To Improve Lazy Structure-Activity Relationships for Noncongeneric Compounds. J Chem Inf Model 2007, 47:2035-2043 (Pubitemid 350275071)
10. Wang H, Klinginsmith J, Dong X, Lee AC, Guha R, Wu Y, Crippen GM, Wild DJ: Chemical Data Mining of the NCI Human Tumor Cell Line Database. J Chem Inf Model 2007, 47:2063-2076 (Pubitemid 350275073)
11. Liu B, Hsu W, Ma Y: Integrating Classification and Association Rule Mining. In KDD'98, 1998. 1998:80-86
12. Li W, Han J, Pei J: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In First IEEE International Conference on Data Mining (ICDM'01). 2001:369-376
13. Thabtah F: A review of associative classification mining. Knowl Eng Rev 2007, 22:37-65 (Pubitemid 46825798)
14. Tamura M, D'haeseleer P: Microbial genotype-phenotype mapping by class association rule mining. Bioinformatics 2008, 24:1523-1529 (Pubitemid 351911704)
15. Creighton C, Hanash S: Mining gene expression databases for association rules. Bioinformatics 2003, 19:79-86 (Pubitemid 36150183)
16. Becquet C, Blachon S, Jeudy B, Boulicaut J-F, Gandrillon O: Strongassociation- rule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genome Biol 2002, 3:1-16
17. Martinez R, Pasquier N, Pasquier C: GenMiner: mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics 2008, 24:2643-2644
18. Park S, Reyes J, Gilbert D, Kim J, Kim S: Prediction of protein-protein interaction types using association rule based classification. BMC Bioinforma 2009, 10:36
19. Leung K-S, Wong K-C, Chan T-M, Wong M-H, Lee K-H, Lau C-K, Tsui SKW: Discovering protein-DNA binding sequence patterns using association rule mining. Nucleic Acids Res 2010, 38:6324-6337
20. Deshpande M, Kuramochi M, Wale N: Frequent Substructure-Based Approaches for Classifying Chemical Compounds. IEEE Trans Knowl Data Eng 2005, 17:1036-1050 (Pubitemid 41259847)
21. Takigawa I, Tsuda K, Mamitsuka H: Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network. PLoS One 2011, 6:e16999
22. Borgelt C, Berthold MR: Proceedings of the 2002 IEEE International Conference on Data Mining; IEEE Computer Society. 2002:51-58
23. Dehaspe L, Toivonen H, King RD: Finding frequent substructures in chemical compounds. In KDD'98, 1998.; 1998:30-36
24. Horváth T, Ramon J, Wrobel S: Frequent subgraph mining in outerplanar graphs. Data Min Knowl Disc 2010, 21:472-508
25. Xiaoxin Yin JH: CPAR: Classification based on Predictive Association Rules. In SDM'2003, 2003
26. SIAM. 2003:331-335
27. Thabtah FA, Cowling P, Peng Y, Rastogi R, Morik K, Bramer M, Wu X: MMAC: A new multi-class, multi-label associative classification approach. In ICDM; 2004:217-224 (Pubitemid 40731034)
28. Thabtah F, Cowling P, Peng Y: Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications; IEEE Computer Society. 2005:127-133
29. Bouzouita I, Elloumi S, Yahia S: GARC: A New Associative Classification Approach. In Data Warehousing and Knowledge Discovery; 2006:554-565 (Pubitemid 44811928)
30. Agrawal R, Imielinski T, Swami A: Mining associations between sets of items in massive databases. In Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data, 1993. 1993:207-216
31. Han J, Pei J, Yin Y: Mining frequent patterns without candidate generation. SIGMOD Rec 2000, 29:1-12
32. Zaki MJ: Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 2000, 12:372-390
33. PKKB. http://cadd.suda.edu.cn/admet/downloads/hERG (accessed November 13, 2011)
34. Li Q.,Jørgensen FS, Oprea T, Brunak S, Taboureau O: hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors. Mol Pharm 2008, 5:117-127 (Pubitemid 351300041)
35. WOMBAT-PK: http://www.sunsetmolecular.com/index.php?option=com- content&view=article&id=16&Itemid=11 (accessed November 15, 2012)
36. Kazius J, McGuire R, Bursi R: Derivation and Validation of Toxicophores for Mutagenicity Prediction. J Med Chem 2004, 48:312-320
37. PubChem. http://pubchem.ncbi.nlm.nih.gov/ (accessed May 20, 2011)
38. Li Q., Wang Y, Bryant SH: A novel method for mining highly imbalanced high-throughput screening data in PubChem. Bioinformatics 2009, 25:3310-3316
39. Han L, Wang Y, Bryant S: Developing and validating predictive decision tree models from mining chemical structural fingerprints and highthroughput screening data in PubChem. BMC Bioinforma 2008, 9:401
40. Accelrys. http://accelrys.com/products/pipeline-pilot/
41. Chemistry Development Kit, version 1.4.5. http://sourceforge.net/ projects/cdk/ files/cdk/ (accessed November 01, 2011)
42. Rapid-I. http://rapid-i.com/content/view/181/190/
43. Dougherty J, Kohavi R, Sahami M: Supervised and Unsupervised Discretization of Continuous Features. In MACHINE LEARNING: PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE. San Francisco, CA: Morgan Kaufmann; 1995:194-202
44. Fatih, Kaya: Discretizing Continuous Features for Naive Bayes and C4.5 Classifiers: Department of Computer Science University of Maryland-College Park; 2007. http://www.cs.umd.edu/Grad/scholarlypapers/papers/fatih-kaya. pdf (accessed Aug 15, 2010)
45. Coenen F: LUCS KDD implementation of CPAR,CMAR and CBA. http://www. csc.liv.ac.uk/~frans/KDD/Software/ (accessed December 12, 2010)
46. Liu B, Hong Jun L, Hsu W, Huan L, Ke W, Setiono R: CBA, 2.1. http://www.comp.nus.edu.sg/~dm2/research-proj.html (accessed November 13, 2010)
47. Gramatica P: Principles of QSAR models validation: internal and external. QSAR Comb Sci 2007, 26:694-701 (Pubitemid 46932857)
48. Durant JL, Leland BA, Henry DR, Nourse JG: Reoptimization of MDL Keys for Use in Drug Discovery. J Chem Inf Comput Sci 2002, 42:1273-1280 (Pubitemid 35468313)
49. DAYLIGHT. http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (accessed Oct 03, 2012)
50. Bartzatt RL: Novel anti-tuberculosis drug designs by data mining for similarity in substituent substitution and structure modification. Drugs and Therapy Studies 2011, 1:57-60
51. Nayyar A, Patel SR, Shaikh M, Coutinho E, Jain R: Synthesis, antituberculosis activity and 3D-QSAR study of amino acid conjugates of 4-(adamantan-1-yl) group containing quinolines. European Journal of Medicinal Chemistry 2009, 44:2017-2029
52. van den Boogaard J, Kibiki GS, Kisanga ER, Boeree MJ, Aarnoutse RE: New Drugs against Tuberculosis: Problems, Progress, and Evaluation of Agents in Clinical Development. Antimicrob Agents Chemother 2009, 53:849-862
53. Tao F, Murtagh F, Farid M: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. Washington, D.C: ACM; 2003:661-666
54. Khan MS: In A Weighted Utility Framework for Mining Association Rules. 2008. 0th edition. Edited by Maybin M, Frans C.; 2008:87-92
55. Soni S, Pillai J, Vyas OP, Nature I, Computing BI: NaBIC 2009. World Congress on 2009, 2009:1492-1496
56. Marinica C, Guillet F, Briand H: Post-Processing of Discovered Association Rules Using Ontologies. In Data Mining Workshops, 2008. ICDMW '08. IEEE International Conference on, 15-19 Dec. 2008, 2008.; 2008:126-133
57. Marinica C, Guillet F: Knowledge-Based Interactive Postmining of Association Rules Using Ontologies. Knowledge and Data Engineering, IEEE Transactions 2010, 22:784-797
58. Unified Medical Language System. http://www.nlm.nih.gov/research/umls/ (accessed April 24, 2011)
59. The Gene Ontology. http://www.geneontology.org/ (accessed March 25, 2011)
60. ChEBI. www.ebi.ac.uk/chebi/ (accessed March 20, 2011)
61. Hastings J, Chepelev L, Willighagen E, Adams N, Steinbeck C, Dumontier M: The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web. PLoS One 2011, 6:e25513.