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EY Chang (2011)
Foundations of Large-Scale Multimedia Information Management and Retrieval
G Shafer, V Vovk (2008)
A tutorial on conformal predictionJ. Mach. Learn Res., 9
C-C Chang, C-J Lin (2011)
LIBSVM: A library for support vector machinesACM Trans. Intell. Syst. Technol., 2
K Woodsend, J Gondziom (2009)
Hybrid MPI/OpenMP parallel linear support vector machine trainingJ. Mach. Learn. Res., 10
A Gammerman, V Vovk (2007)
Hedging predictions in machine learningComput. J., 50
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, É Duchesnay (2011)
Scikit-learn: Machine learning in PythonJ. Mach. Learn. Res., 12
Y You, H Fu, SL Song, A Randles, D Kerbyson, A Marquez, G Yang, A Hoisie (2015)
Scaling support vector machines on modern HPC platformsJ. Parallel Distrib. Comput., 76
V Vovk, A Gammerman, G Shafer (2005)
Algorithmic Learning in a Random World
V Monev (2004)
Introduction to similarity searching in chemistryComm. Math. Comp. Chem., 51
AN Jain, A Nicholls (2008)
Recommendations for evaluation of computational methodsJ. Comput. Aided Mol. Des., 22
Y Wang, T Suzek, J Zhang, J Wang, S He, T Cheng, BA Shoemaker, A Gindulyte, SH Bryant (2014)
Pubchem BioAssay: 2014 upyearNucleic Acids Res., 42
J-L Faulon, DP Visco, RS Pophale (2003)
The signature molecular descriptor. 1. using extended valence sequences in qsar and qspr studiesJ. Chem. Inf. Comput. Sci., 43
DC Weis, DP Visco (2008)
Jean-loup Faulon. Data mining pubchem using a support vector machine with the signature molecular descriptor Classification of factor {XIa} inhibitorsJ. Mol. Graph. Model., 27
The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Jun 16, 2017
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