Rough sets for selection of molecular descriptors to predict biological activity of molecules

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 40, Issue 6, 2010, Pages 639-648

  Maji, Pradipta ^a   Paul, Sushmita ^a  

^a INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Drug design; feature selection; quantitative structure activity relationship (QSAR); rough set; support vector machine (SVM)

Indexed keywords

DRUG DESIGN; FEATURE SELECTION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS; ROUGH SET; SUPPORT VECTOR MACHINE (SVM);

ALGORITHMS; FEATURE EXTRACTION; MACHINE DESIGN; SUPPORT VECTOR MACHINES;

ROUGH SET THEORY;

EID: 77958150996     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2010.2047943     Document Type: Article

References (42)

1. J. Bajorath, T. E. Klein, T. P. Lybrand, and J. Novotny, "Computer-aided drug discovery: From target proteins to drug candidates," in Pac. Symp. Biocomput. , 1999, vol. 4, pp. 413-414.
2. A. R. Leach, Molecular Modelling: Principles and Applications, vol. 2. Englewood Cliffs, NJ: Prentice-Hall, 2001.
3. R. Guha and P. C. Jurs, "Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors," J. Chem. Inf. Comput. Sci. , vol. 44, pp. 2179-2189, 2004.
4. I. V. Tetkoa, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V. A. Palyulin, E. V. Radchenko, N. S. Zefirov, A. S. Makarenko, V. Y. Tanchuk, and V. V. Prokopenko, "Virtual computational chemistry laboratory design and description," J. Comput.-Aided Mol. Des. , vol. 19, pp. 453-463, 2005.
5. A. R. Katritzky,V. Lobanov, andM. karelson, "Comprehensive descriptors for structural and statistical analysis version 1. 1," Tech. Rep. , Univ. Florida, 1994.
6. Z. R. Li, L. Y. Han, Y. Xue, C. W. Yap, H. Li, L. Jiang, and Y. Z. Chen, "MODEL-molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds," Biotechnol. Bioeng. , vol. 97, pp. 389-396, 2007.
7. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. Englewood Cliffs, NJ: Prentice-Hall, 1982.
8. M. Ozdemir, M. J. Embrechts, F. Arciniegas, C. M. Breneman, L. Lockwood, and K. P. Bennett, "Feature selection for in-silico drug design using genetic algorithms and neural networks," in Proc. IEEE Mt Workshop Soft Comput. Ind. Appl. , 2001, pp. 25-27.
9. R. Guha and P. C. Jurs, "Development of QSAR models to predict and interpret the biological activity of artemisinin analogues," J. Chem. Inf. Comput. Sci. , vol. 44, pp. 1440-1449, 2004.
10. R. Leardi and A. L. Gonzalez, "Genetic algorithms applied to feature selection in PLS regression: How and when to use them," Chemometrics Intell. Lab. Syst. , vol. 41, pp. 195-207, 1998.
11. V. Sventik, T. Wang, C. Tong, A. Liaw, R. P. Sheridan, and Q. Song, "Boosting: An ensemble learning tool for compound classification and QSAR modeling," J. Chem. Inf. Model. , vol. 45, no. 3, pp. 786-799, 2005.
12. A. N. Jain, K. Koile, and D. Chapman, "Compass: Predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark," J. Med. Chem. , vol. 37, pp. 2315-2327, 1994.
13. K. Tuppurainen, M. Viisas, R. Laatikainen, and M. Peräkylä, "Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: Validation using a benchmark steroid data set," J. Chem. Inf. Comput. Sci. , vol. 42, pp. 607-613, 2002.
14. D. B. Turner, P. Willett, A. M. Ferguson, and T. W. Heritage, "Evaluation of a novel molecular vibration-based descriptor (EVA) for QSAR studies: 2. Model validation using a benchmark steroid dataset," J. Comput.-Aided Mol. Des. , vol. 13, pp. 271-296, 1999.
15. G. Bravi, E. Gancia, P. Mascagni,M. Pegna, R. Todeschini, and A. Zaliani, "MS-WHIM: New 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids," J. Comput.-Aided Mol. Des. , vol. 11, pp. 79-92, 1997.
16. D. Robert, L. Amat, and R. Carbo-Dorca, "Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: Prediction of the corticosteroid-binding globulin binding affinity for a steroid family," J. Chem. Inf. Comput. Sci. , vol. 39, pp. 333-344, 1999.
17. V. Uddameri and M. Kuchanur, "Fuzzy QSARs for predicting log Koc of persistent organic pollutants," Chemosphere, vol. 54, pp. 771-776, 2004.
18. M. Kumar, K. Thurow, N. Stoll, and R. Stoll, "Robust fuzzy mappings for QSAR studies," Eur. J. Med. Chem. , vol. 42, pp. 675-685, 2007.
19. C. D. N. Neagu, A. O. Aptula, and G. Gini, "Neural and neuro-fuzzy models of toxic action of phenols," in Proc. First Int. IEEE Symp. Intell. Syst. , 2002, vol. 1, pp. 283-288.
20. Y. P. Zhou, C. B. Cai, S. Huan, J. H. Jiang, H. L. Wu, G. L. Shen, and R. Q. Yu, "QSAR study of angiotensin II antagonists using robust boosting partial least squares regression," Analytica Chimica Acta, vol. 593, pp. 68-74, 2007.
21. Z. Pawlak, Rough Sets, Theoretical Aspects of Resoning About Data. Dordrecht, The Netherlands: Kluwer, 1991.
22. A. Skowron, R. Swiniarski, and P. Synak, "Approximation spaces and information granulation," in Transactions on Rough Sets III, vol. 3400, J. F. Peters, A. Skowron, and A. D. Van, Eds. Heidelberg, Germany: Springer-Verlag, 2005, pp. 175-189.
23. W. Xiaolong, Q. Chen, and D. S. Yeung, "Mining pinyin-to-character conversion rules from large-scale corpus: A rough set approach," IEEE Trans. Syst. , Man Cybern. , Part B, Cybern. , vol. 34, no. 2, pp. 834-844, Apr. 2004.
24. F. Fernandez-Riverola, F. Diaz, and J. M. Corchado, "Reducing the memory size of a fuzzy case-based reasoning system applying rough set techniques," IEEE Trans. Syst. , Man, Cybern. , Part C: Appl. Rev. , vol. 37, no. 1, pp. 138-146, Jan. 2007.
25. P. Maji and S. K. Pal, "Rough set based generalized fuzzy C-means algorithm and quantitative indices," IEEE Trans. Syst. , Man Cybern. , Part B, Cybern. , vol. 37, no. 6, pp. 1529-1540, Dec. 2007.
26. Q. E. Wu, W. Tuo, H. Y. Xuan, and L. J. Sheng, "Topology theory on rough sets," IEEE Trans. Syst. , Man Cybern. , Part B, Cybern. , vol. 38, no. 1, pp. 68-77, Feb. 2008.
27. R. Jensen and Q. Shen, "Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approach," IEEE Trans. Knowl. Data Eng. , vol. 16, no. 12, pp. 1457-1471, Dec. 2004.
28. A. Chouchoulas and Q. Shen, "Rough set-aided keyword reduction for text categorisation," Appl. Artif. Intell. , vol. 15, pp. 843-873, 2001.
29. A. Skowron and C. Rauszer, "The discernibility matrices and functions in information systems," in Intelligent Decision Support, R. Slowinski, Ed. Dordrecht, The Netherlands: Kluwer, 1992, pp. 331-362.
30. W. Ziarko, "Variable precision rough set model," J. Comput. Syst. Sci. , vol. 46, pp. 39-59, 1993.
31. J. Bazan, A. Skowron, and P. Synak, "Dynamic reducts as a tool for extracting laws from decision tables," in Proc. 8th Symp. Methodologies Intell. Syst. , 1994, pp. 346-355.
32. A. Bjorvand and J. Komorowski, "Practical applications of genetic algorithms for efficient reduct computation," in Proc. 15th IMACS World Congr. Sci. Comput. , Model. Appl. Math. , 1997, vol. 4, pp. 601-606.
33. D. ́ Slȩzak, "Approximate reducts in decision tables," in Proc. 6th Int. Conf. Inf. Process. Manage. Uncertainty Knowl.-Based Syst. , 1996, pp. 1159-1164.
34. J. Wŕoblewski, "Finding minimal reducts using genetic algorithms," in Proc. 2nd Annu. Joint Conf. Inf. Sci. , 1995, pp. 186-189.
35. N. Zhong, J. Dong, and S. Ohsuga, "Using rough sets with heuristics for feature selection," J. Intell. Inf. Syst. , vol. 16, pp. 199-214, 2001.
36. P. Maji, "f-information measures for efficient selection of discriminative genes from microarray data," IEEE Trans. Biomed. Eng. , vol. 56, no. 4, pp. 1063-1069, Apr. 2009.
37. V. N. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
38. H. Chen, J. Zhou, and G. Xie, "PARM: A genetic algorithm to predict bioactivity," J. Chem. Inf. Comput. Sci. , vol. 38, pp. 243-250, 1998.
39. D. Robinson, P. Winn, P. Lyne, andW. Richards, "Self-organizing molecular field analysis: A tool for structure-activity studies," J. Med. Chem. , vol. 42, pp. 573-583, 1999.
40. J. Polanski and B. Walczak, "The comparative molecular surface analysis (COMSA): A novel tool for molecular design," Comput. Chem. , vol. 24, pp. 615-625, 2000.
41. S. S. Liu, C. S. Yin, Z. L. Li, and S. X. Cai, "QSAR study of steroid benchmark and dipeptides based on MEDV-13," J. Chem. Inf. Comput. Sci. , vol. 41, pp. 321-329, 2001.
42. L. Amat, E. Besalu, and R. Carbo-Dorca, "Identification of active molecular sites using quantum-self-similarity matrices," J. Chem. Inf. Comput. Sci. , vol. 41, pp. 978-991, 2001.