Predicting a small molecule-kinase interaction map: A machine learning approach

Journal of Cheminformatics

Volumn 3, Issue 6, 2011, Pages

  Buchwald, Fabian ^a   Richter, Lothar ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84862647729     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-3-22     Document Type: Article

References (28)

1. Engvall E, Perlman P: Enzyme-linked immunosorbent assay (ELISA). Quantitative assay of immunoglobulin G. Immunochemistry 1971, 8(9):871-4.
2. LaValle SM, Finn PW, Kavraki LE, Latombe JC: Efficient database screening for rational drug design using pharmacophore-constrained conformational search. Proceedings of the third annual international conference on computational molecular biology, RECOMB'99, April 11-14, Lyon, France 1999, 250-260.
3. Buzko OV, Bishop AC, Shokat KM: Modified AutoDock for accurate docking of protein kinase inhibitors. J Comput-Aided Mol Des 2002, 16(2):113-127. (Pubitemid 34855038)
4. Yap CW, Chen YZ: Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 2005, 45(4):982-992. (Pubitemid 41156697)
5. Helma C, Cramer T, Kramer S, De Raedt L: Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J Chem Inf Model 2004, 44(4):1402-1411
6. Müller KR, Rätsch G, Sonnenburg S, Mika S, Grimm M, Heinrich N: Classifying 'drug-likeness' with kernel-based learning methods. J Chem Inf Model 2005, 45(2):249-253. (Pubitemid 40635328)
7. Fabian MA, Biggs WH, Treiber DK, Atteridge CE, Azimioara MD, Benedetti MG, Carter TA, Ciceri P, Edeen PT, Floyd M, Ford JM, Galvin M, Gerlach JL, Grotzfeld RM, Herrgard S, Insko DE, Insko MA, Lai AG, Léélias JM, Mehta SA, Milanov ZV, Velasco AM, Wodicka LM, Patel HK, Zarrinkar PP, Lockhart DJ: A small molecule-kinase interaction map for clinical kinase inhibitors. Nat Biotechnol 2005, 23:329-336. (Pubitemid 41094421)
8. Kinase, inhibitor data, features, binding matrix. [http://wwwkramer.in. tumde/research/applications/predicting-a-small-molecule-kinase-interactionmap-a- machine-learning-approach/].
9. Agrawal R, Srikant R: Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB'94, September 12-15, Santiago, Chile Edited by: Bocca JB, Jarke M, Zaniolo C Morgan Kaufmann; 1994:487-499.
10. Mannila H, Toivonen H: Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Disc 1997, 1(3):241-258.
11. KinBase: The kinase database at Sugen/Salk. 2008 [http://kinase.com/ kinbase
12. Needleman SB, Wunsch CD: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970, 48:443-453.
13. Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994, 224673-4680.
14. Rückert U, Kramer S: Frequent free tree discovery in graph data. In Proceedings of the 2004 ACM Symposium on Applied Computing (SAC), Nicosia, Cyprus, March 14-17. Edited by: Haddad H, Omicini A, Wainwright RL, Liebrock LM. ACM; 2004:564-570.
15. JOELiB2: A Java based cheminformatics (computational chemistry) library, 2008. [http://www-ra.informatik.uni-tuebingen. de/software/joelib
16. Hansch C, Leo A, Hoekman DH: Exploring QSAR, Fundamentals and Application in Chemistry and Biology Washington, DC, USA: American Chemical Society; 1995.
17. Böhm HJ, Schneider G, Kubinyi H, Mannhold R, Timmerman H: Virtual screening for bioactive molecules Weinheim, New York: Wiley-VCH; 2000.
18. Hand DJ, Mannila H, Smyth P: Principles of data mining Cambridge, MA, USA: The MIT Press; 2001
19. Data Mining Tools See 5 and C5.0, 2008. [http://www.rulequest.com/see5- info.html].
20. Quinlan R: C4.5: Programs for machine learning by J. Ross Quinlan San Francisco, CA, USA: Morgan Kaufmann Publishers, Inc; 1992.
21. Burges CJ: A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 1998, 2:121-167. (Pubitemid 128695475)
22. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: An update. SIGKDD Explorations 2009, 11:10-18.
23. Keerthi S, Shevade S, Bhattacharyya C, Murthy K: Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 2001, 13(3):637-649. (Pubitemid 33595014)
24. NominalToBinary: A Java class for converting nominal features to "binary numeric", 2011. [http://weka.sourceforge.net/doc.stable/weka/ filters/unsupervised/attribute/NominalToBinary.html
25. Karaman MW, Herrgard S, Treiber DK, Gallant P, Atteridge CE, Campbell BT, Chan KW, Ciceri P, Davis MI, Edeen PT, Faraoni R, Floyd M, Hunt JP, Lockhart DJ, Milanov ZV, Morrison MJ, Pallares G, Patel HK, Pritchard S, Wodicka LM, Zarrinkar PP: A quantitative analysis of kinase inhibitor selectivity. Nat Biotechnol 2008, 26:127-132.
26. Briem H, Günther J: Classifying "kinase inhibitor- likeness" by using machine-learning methods. ChemBioChem 2005, 6 (3) 558-66 (Pubitemid 40868145)
27. Xia X, Maliski EG, Gallant P, Rogers D: Classification of kinase inhibitors using a Bayesian model. J Med Chem 2004, 47 (18) 4463-70 (Pubitemid 39096834)
28. Weill N, Rognan D: Development and validation of a novel protein-ligand fingerprint to mine chemogenomic space: Application to G proteincoupled receptors and their ligands. J Chem Inf Model 2009, 49(4):1049-62