Relevance Vector Machines: Sparse Classification Methods for QSAR

Journal of Chemical Information and Modeling

Volumn 55, Issue 8, 2015, Pages 1529-1534

  Burden, Frank R ^a,b   Winkler, David A ^a,b,c,d  

^a CSIRO   (Australia)

^b MONASH UNIVERSITY   (Australia)

^c LA TROBE UNIVERSITY   (Australia)

^d FLINDERS UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHEMISTRY; KNOWLEDGE BASED SYSTEMS; MAXIMUM PRINCIPLE; SUPPORT VECTOR MACHINES;

BAYESIAN REGULARIZATION; CONTINUOUS DATA; EXPECTATION-MAXIMIZATION ALGORITHMS; MACHINE LEARNING METHODS; MODEL INTERPRETATIONS; QUANTITATIVE STRUCTURES; RELEVANCE VECTOR MACHINE; SPARSE CLASSIFICATION;

LEARNING SYSTEMS;

ALGORITHM; BAYES THEOREM; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE;

ALGORITHMS; BAYES THEOREM; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINE;

EID: 84940203137     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.5b00261     Document Type: Article

References (37)

1. Epa, V. C.; Burden, F. R.; Tassa, C.; Weissleder, R.; Shaw, S.; Winkler, D. A. Modeling Biological Activities of Nanoparticles Nano Lett. 2012, 12, 5808-5812 10.1021/nl303144k
2. Epa, V. C.; Hook, A. L.; Chang, C.; Yang, J.; Langer, R.; Anderson, D. G.; Williams, P.; Davies, M. C.; Alexander, M. R.; Winkler, D. A. Modelling and Prediction of Bacterial Attachment to Polymers Adv. Funct. Mater. 2014, 24, 2085-2093 10.1002/adfm.201302877
3. Epa, V. C.; Yang, J.; Mei, Y.; Hook, A. L.; Langer, R.; Anderson, D. G.; Davies, M. C.; Alexander, M. R.; Winkler, D. A. Modelling Human Embryoid Body Cell Adhesion to a Combinatorial Library Of Polymer Surfaces J. Mater. Chem. 2012, 22, 20902-20906 10.1039/c2jm34782b
4. Le, T.; Epa, V. C.; Burden, F. R.; Winkler, D. A. Quantitative Structure-Property Relationship Modeling of Diverse Materials Properties Chem. Rev. 2012, 112, 2889-2919 10.1021/cr200066h
5. Winkler, D. A.; Burden, F. R. Robust, Quantitative Tools for Modelling Ex-Vivo Expansion of Haematopoietic Stem Cells and Progenitors Mol. BioSyst. 2012, 8, 913-920 10.1039/c2mb05439f
6. Burden, F. R.; Winkler, D. A. Robust QSAR Models using Bayesian Regularized Neural Networks J. Med. Chem. 1999, 42, 3183-3187 10.1021/jm980697n
7. Burden, F. R.; Winkler, D. A. New QSAR Methods Applied to Structure-Activity Mapping and Combinatorial Chemistry J. Chem. Inf. Model. 1999, 39, 236-242 10.1021/ci980070d
8. Burden, F. R.; Winkler, D. A. An Optimal Self-Pruning Neural Network and Nonlinear Descriptor Selection in QSAR QSAR Comb. Sci. 2009, 28, 1092-1097 10.1002/qsar.200810202
9. Burden, F. R.; Winkler, D. A. Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods QSAR Comb. Sci. 2009, 28, 645-653 10.1002/qsar.200810173
10. Cortes, C.; Vapnik, V. Support-Vector Networks Mach. Learn. 1995, 20, 273-297 10.1007/BF00994018
11. Noble, W. S. What is a Support Vector Machine? Nat. Biotechnol. 2006, 24, 1565-1567 10.1038/nbt1206-1565
12. Doucet, J.-P.; Barbault, F.; Xia, H.; Panaye, A.; Fan, B. Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design Curr. Comput.-Aid. Drug. Des. 2007, 3, 263-289
13. Winkler, D. A.; Burden, F. R. Robust QSAR Models from Novel Descriptors and Bayesian Regularised Neural Networks Mol. Simul. 2000, 24, 243-+ 10.1080/08927020008022374
14. Winkler, D. A.; Burden, F. R. Modelling Blood-Brain Barrier Partitioning using Bayesian Neural Nets J. Mol. Graphics Modell. 2004, 22, 499-505 10.1016/j.jmgm.2004.03.010
15. Autefage, H.; Gentleman, E.; Littmann, E.; Hedegaard, M. A.; Von Erlach, T.; O'Donnell, M.; Burden, F. R.; Winkler, D. A.; Stevens, M. M. Sparse Feature Selection Methods Identify Unexpected Global Cellular Response to Strontium-Containing Materials Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 4280-5 10.1073/pnas.1419799112
16. Celiz, A. D.; Smith, J. G. W.; Langer, R.; Anderson, D. G.; Winkler, D. A.; Barrett, D. A.; Davies, M. C.; Young, L. E.; Denning, C.; Alexander, M. R. Materials for Stem Cell Factories of the Future Nat. Mater. 2014, 13, 570-579 10.1038/nmat3972
17. Huh, Y. H.; Noh, M.; Burden, F. R.; Chen, J. C.; Winkler, D. A.; Sherley, J. L. Sparse Feature Selection Identifies H2A.Z as a Novel, Pattern-Specific Biomarker for Asymmetrically Self-Renewing Distributed Stem Cells Stem Cell Res. 2015, 14, 144-54 10.1016/j.scr.2014.12.007
18. Le, T. C.; Ballard, M.; Casey, P.; Liu, M. S.; Winkler, D. A. Illuminating Flash Point: Comprehensive Prediction Models Mol. Inf. 2015, 34, 18-27 10.1002/minf.201400098
19. Le, T. C.; Mulet, X.; Burden, F. R.; Winkler, D. A. Predicting the Complex Phase Behavior of Self-Assembling Drug Delivery Nanoparticles Mol. Pharmaceutics 2013, 10, 1368-1377 10.1021/mp3006402
20. Salahinejad, M.; Le, T. C.; Winkler, D. A. Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help? Mol. Pharmaceutics 2013, 10, 2757-2766 10.1021/mp4001958
21. Winkler, D. A.; Breedon, M.; Hughes, A. E.; Burden, F. R.; Barnard, A. S.; Harvey, T. G.; Cole, I. Towards Chromate-Free Corrosion Inhibitors: Structure-Property Models for Organic Alternatives Green Chem. 2014, 16, 3349-3357 10.1039/c3gc42540a
22. Winkler, D. A.; Burden, F. R.; Yan, B.; Weissleder, R.; Tassa, C.; Shaw, S.; Epa, V. C. Modelling and Predicting the Biological Effects of Nanomaterials SAR QSAR Environ. Res. 2014, 25, 161-172 10.1080/1062936X.2013.874367
23. Tipping, M. E. Sparse Bayesian Learning and the Relevance Vector Machine J. Mach. Learn. Res. 2001, 1, 211-244
24. Burden, F. R. Quantitative structure-Activity Relationship Studies using Gaussian Processes J. Chem. Inf. Model. 2001, 41, 830-835 10.1021/ci000459c
25. Burden, F. R.; Ford, M. G.; Whitley, D. C.; Winkler, D. A. Use of Automatic Relevance Determination in QSAR Studies using Bayesian Neural Networks J. Chem. Inf. Model. 2000, 40, 1423-1430 10.1021/ci000450a
26. Rafi, M.; Shaikh, M. S. A Comparison of SVM and RVM for Document Classification. 5, 2013; http://arxiv.org/abs/1301.2785 (accessed 24 December 2014).
27. Harrison, D.; Rubinfeld, D. L. Hedonic Housing Prices and Demand for Clean-Air J. Environ. Econ. Manag. 1978, 5, 81-102 10.1016/0095-0696(78)90006-2
28. Smith, J. W.; Everhart, J. E.; Dickson, W. C.; Knowler, W. C.; Johannes, R. S. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. In Symposium on Computer Applications and Medical Care; 1988; IEEE Computer Science Press: 1988; pp 261-265.
29. Katritzky, A. R.; Slavov, S. H.; Dobchev, D. A.; Karelson, M. Rapid QSPR Model Development Technique for Prediction of Vapor Pressure of Organic Compounds Comput. Chem. Eng. 2007, 31, 1123-1130 10.1016/j.compchemeng.2006.10.001
30. Burden, F. R.; Winkler, D. A. A Quantitative Structure-Activity Relationships Model for the Acute Toxicity of Substituted Benzenes to Tetrahymena Pyriformis using Bayesian-Regularized Neural Networks Chem. Res. Toxicol. 2000, 13, 436-440 10.1021/tx9900627
31. Winkler, D. A.; Burden, F. R.; Watkins, A. J. R. Atomistic Topological Indices Applied to Benzodiazepines using Various Regression Methods Quant. Struct.-Act. Relat. 1998, 17, 14-19 10.1002/(SICI)1521-3838(199801)17:01<14::AID-QSAR14>3.0.CO;2-U
32. Wolberg, W. H.; Mangasarian, O. L. Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology Proc. Natl. Acad. Sci. U. S. A. 1990, 87, 9193-9196 10.1073/pnas.87.23.9193
33. Mansouri, K.; Ringsted, T.; Ballabio, D.; Todeschini, R.; Consonni, V. Quantitative Structure-Activity Relationship Models for Ready Biodegradability of Chemicals J. Chem. Inf. Model. 2013, 53, 867-878 10.1021/ci4000213
34. Figueiredo, M. A. T. Adaptive Sparseness for Supervised Learning IEEE Trans. Patt. Anal. 2003, 25, 1150-1159 10.1109/TPAMI.2003.1227989
35. Le, T. C.; Conn, C. E.; Burden, F. R.; Winkler, D. A. Computational Modeling and Prediction of the Complex Time-Dependent Phase Behavior of Lyotropic Liquid Crystals under in Meso Crystallization Conditions Cryst. Growth Des. 2013, 13, 1267-1276 10.1021/cg301730z
36. Liu, Y. Y.; Winkler, D. A.; Epa, V. C.; Zhang, B.; Yan, B. Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries Nano Res. 2015, 8, 1293-1308 10.1007/s12274-014-0618-5
37. Salahinejad, M.; Le, T. C.; Winkler, D. A. Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds J. Chem. Inf. Model. 2013, 53, 223-229 10.1021/ci3005012