A nonparametric weighted feature extraction-based method for c-Jun N-terminal kinase-3 inhibitor prediction

Imani, 2017, Phishing website detection using weighted feature line embedding, ISC Int. J. Inf. Secur., 9, 147 Pi-Fuei, 2006, A linear feature extraction for multiclass classification problems based on class mean and covariance discriminant information, IEEE Trans. Pattern Anal. Mach. Intell., 28, 223, 10.1109/TPAMI.2006.26 Richards, 2005, Analysis of remotely sensed data: the formative decades and the future, IEEE Trans. Geosci. Remote Sens., 43, 422, 10.1109/TGRS.2004.837326 Kuo, 2009, Kernel nonparametric weighted feature extraction for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 47, 1139, 10.1109/TGRS.2008.2008308 Imani, 2015, Feature extraction using weighted training samples, IEEE Geosci. Remote Sens. Lett., 12, 1387, 10.1109/LGRS.2015.2402167 Kuo, 2004, Nonparametric weighted feature extraction for classification, IEEE Trans. Geosci. Remote Sens., 42, 1096, 10.1109/TGRS.2004.825578 Qiao, 2017, Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts, Food Chem., 220, 393, 10.1016/j.foodchem.2016.09.119 Sahoo, 2007, Using the one-dimensional S-transform as a discrimination tool in classification of hyperspectral images, Can. J. Remote Sens., 33, 551, 10.5589/m07-057 Lin, 2008, Nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses, EURASIP J. Appl. Signal Process., 2008, 849040, 10.1155/2008/849040 Wang, 2013, A new method for EEG-based concealed information test, IEEE Trans. Inf. Forensics Secur., 8, 520, 10.1109/TIFS.2013.2244884 Lin, 2010, Recognizing human actions using NWFE-based histogram vectors, EURASIP J. Appl. Signal Process., 2010, 9 Xing, 2014, A real-time EMG pattern recognition method for virtual myoelectric hand control, Neurocomputing, 136, 345, 10.1016/j.neucom.2013.12.010 Svetnik, 2005, Boosting:  an ensemble learning tool for compound classification and QSAR modeling, J. Chem. Inf. Model., 45, 786, 10.1021/ci0500379 Singh, 2014, Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology, Chem. Res. Toxicol., 27, 741, 10.1021/tx400371w Cerruela García, 2018, Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers, SAR QSAR Environ. Res., 29, 187, 10.1080/1062936X.2017.1423376 Plewczynski, 2006, Assessing different classification methods for virtual screening, J. Chem. Inf. Model., 46, 1098, 10.1021/ci050519k Ehrman, 2007, Virtual screening of Chinese herbs with random forest, J. Chem. Inf. Model., 47, 264, 10.1021/ci600289v Qing-You, 2007, Random forest prediction of mutagenicity from empirical physicochemical descriptors, J. Chem. Inf. Model., 47, 1, 10.1021/ci050520j Ijjaali, 2007, Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology, Bioorg. Med. Chem., 15, 4256, 10.1016/j.bmc.2007.03.062 Gupta, 1996, Selective interaction of JNK protein kinase isoforms with transcription factors, EMBO J., 15, 2760, 10.1002/j.1460-2075.1996.tb00636.x Mohit, 1995, p493F12 kinase: a novel MAP kinase expressed in a subset of neurons in the human nervous system, Neuron, 14, 67, 10.1016/0896-6273(95)90241-4 Kyung, 2010, Pathological roles of MAPK signaling pathways in human diseases, Biochim. Biophys. Acta (BBA) - Mol. Basis Dis., 1802, 396, 10.1016/j.bbadis.2009.12.009 Salh, 2007, c-Jun N-terminal kinases as potential therapeutic targets, Expert Opin. Ther. Targets, 11, 1339, 10.1517/14728222.11.10.1339 Stocks, 2005, Structure-driven HtL: design and synthesis of novel aminoindazole inhibitors of c-Jun N-terminal kinase activity, Bioorg. Med. Chem. Lett., 15, 3459, 10.1016/j.bmcl.2005.05.008 Gang, 2006, Aminopyridine carboxamides as c-Jun N-terminal kinase inhibitors: targeting the gatekeeper residue and beyond, Bioorg. Med. Chem. Lett, 16, 5723, 10.1016/j.bmcl.2006.08.097 Gaillard, 2005, Design and synthesis of the first generation of novel potent, selective, and in vivo active (benzothiazol-2-yl) acetonitrile inhibitors of the c-Jun N-terminal kinase, J. Med. Chem., 48, 4596, 10.1021/jm0310986 Szczepankiewicz, 2006, Aminopyridine-based c-Jun N-terminal kinase inhibitors with cellular activity and minimal cross-kinase activity, J. Med. Chem., 49, 3563, 10.1021/jm060199b Shaikh, 2006, Three-dimensional quantitative structure–activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors, Bioorg. Med. Chem. Lett, 16, 5917, 10.1016/j.bmcl.2006.06.039 Xiang-Xiang, 2014, Molecular modeling studies of JNK3 inhibitors using QSAR and docking, Med. Chem. Res., 23, 2456, 10.1007/s00044-013-0782-2 Rao, 2012, Molecular docking studies on JNK inhibitors at the allosteric JNK-JIP interaction site, Curr. Sci., 102, 1009 Yi, 2008, 3D-QSAR and docking studies of aminopyridine carboxamide inhibitors of c-Jun N-terminal kinase-1, Eur. J. Med. Chem., 43, 604, 10.1016/j.ejmech.2007.04.020 Schattel, 2011, Modeling and benchmark data set for the inhibition of c-Jun N-terminal kinase-3, J. Chem. Inf. Model., 51, 670, 10.1021/ci100410h Steinbeck, 2006, Recent developments of the chemistry development kit (CDK)-an open-source java library for chemo-and bioinformatics, Curr. Pharmaceut. Des., 12, 2111, 10.2174/138161206777585274 Rogers, 2010, Extended-connectivity fingerprints, J. Chem. Inf. Model., 50, 742, 10.1021/ci100050t Durant, 2002, Reoptimization of MDL keys for use in drug discovery, J. Chem. Inf. Comput. Sci., 42, 1273, 10.1021/ci010132r E-State fragments in E-State Fingerprint . Available online: http://www.scbdd.com/padel_desc/fps-estate/(accessed on 8 january 2019). Downs, 1989, Review of ring perception algorithms for chemical graphs, J. Chem. Inf. Comput. Sci., 29, 172, 10.1021/ci00063a007 Wei, 2011, PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem., 32, 1466, 10.1002/jcc.21707 Qinbao, 2013, A fast clustering-based feature subset selection algorithm for high-dimensional data, IEEE Trans. Knowl. Data Eng., 25, 1, 10.1109/TKDE.2011.181 Fourches, 2010, Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model., 50, 1189, 10.1021/ci100176x Varmuza, 2014, KNN classification—evaluated by repeated double cross validation: recognition of minerals relevant for comet dust, Chemometr. Intell. Lab. Syst., 138, 64, 10.1016/j.chemolab.2014.07.011 Ishibuchi, 2013, Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design, Knowl. Based Syst., 54, 22, 10.1016/j.knosys.2013.09.023 Filzmoser, 2009, Repeated double cross validation, J. Chemom., 23, 160, 10.1002/cem.1225 Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn., 30, 1145, 10.1016/S0031-3203(96)00142-2 García-Pedrajas, 2007, Immune network based ensembles, Neurocomputing, 70, 1155, 10.1016/j.neucom.2006.11.005 Rodriguez, 2006, Rotation forest: a new classifier ensemble method, IEEE Trans. Pattern Anal. Mach. Intell., 28, 1619, 10.1109/TPAMI.2006.211 Bor-Chen, 2003, Regularized feature extractions for hyperspectral data classification Salzberg, 1994, vol 16, 235 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol. (TIST), 2, 27 Butina, 1999, Unsupervised data base clustering based on daylight's fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large data sets, J. Chem. Inf. Comput. Sci., 39, 747, 10.1021/ci9803381 Rokach, 2005, Clustering methods, 321 Friedman, 2000, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), Ann. Stat., 28, 337, 10.1214/aos/1016218223 Maudes, 2009, Disturbing neighbors diversity for decision forests, 113