Adaptive variable-weighted support vector machine as optimized by particle swarm optimization algorithm with application of QSAR studies.
暂无分享,去创建一个
Hai-Long Wu | Guo-Li Shen | Ru-Qin Yu | Li-Juan Tang | Jian-Hui Jiang | Jian-hui Jiang | G. Shen | R. Yu | Hai-Long Wu | Li-Juan Tang | Ke-Jun Zhong | Jian-Hui Wen | Ke-jun Zhong | Jian-hui Wen
[1] K. Roy,et al. QSAR by LFER model of cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives using principal component factor analysis and genetic function approximation. , 2005, Bioorganic & medicinal chemistry.
[2] Alexander Golbraikh,et al. Combinatorial QSAR Modeling of P-Glycoprotein Substrates , 2006, J. Chem. Inf. Model..
[3] Ronald D. Snee,et al. Validation of Regression Models: Methods and Examples , 1977 .
[4] Ruisheng Zhang,et al. QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine , 2004, J. Chem. Inf. Model..
[5] Jian-Hui Jiang,et al. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: Application in QSAR studies of bioactivity of organic compounds , 2007, J. Comput. Chem..
[6] Jian-Hui Jiang,et al. Radial Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies , 2007, J. Chem. Inf. Model..
[7] Quan Pan,et al. Classification of protein quaternary structure with support vector machine , 2003, Bioinform..
[8] R. Bhat,et al. GSK3β Signalling: Casting a Wide Net in Alzheimer’s Disease , 2002, Neurosignals.
[9] Igor V. Pletnev,et al. Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions , 2003, J. Chem. Inf. Comput. Sci..
[10] R. J. Doerksen,et al. Probing the physicochemical and structural requirements for glycogen synthase kinase-3alpha inhibition: 2D-QSAR for 3-anilino-4-phenylmaleimides. , 2006, Bioorganic & medicinal chemistry.
[11] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[12] H. M. Vinkers,et al. Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination. , 2005, Talanta.
[13] Chu-Young Kim,et al. Structural aspects of isozyme selectivity in the binding of inhibitors to carbonic anhydrases II and IV. , 2002, Journal of medicinal chemistry.
[14] Wei-Qi Lin,et al. Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies. , 2007, Talanta.
[15] Zhide Hu,et al. Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. , 2007, Talanta.
[16] Carbonic Anhydrase Inhibitors: Synthesis of Water Soluble Sulfonamides Incorporating a 4-sulfamoylphenylmethylthiourea Scaffold, with Potent Intraocular Pressure Lowering Properties , 2002, Journal of enzyme inhibition and medicinal chemistry.
[17] Wen Du,et al. New Variable Selection Method Using Interval Segmentation Purity with Application to Blockwise Kernel Transform Support Vector Machine Classification of High-Dimensional Microarray Data , 2009, J. Chem. Inf. Model..
[18] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[19] Christina A. Wilson,et al. GSK-3α regulates production of Alzheimer's disease amyloid-β peptides , 2003, Nature.
[20] Jian-Hui Jiang,et al. Optimized Block-wise Variable Combination by Particle Swarm Optimization for Partial Least Squares Modeling in Quantitative Structure-Activity Relationship Studies , 2005, J. Chem. Inf. Model..
[21] Jesús Avila,et al. Glycogen synthase kinase 3: a drug target for CNS therapies , 2004, Journal of neurochemistry.
[22] Jian Jiao,et al. Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees , 2009, J. Chem. Inf. Model..
[23] G. Melagraki,et al. QSAR study on para-substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices. , 2006, Bioorganic & medicinal chemistry.
[24] Hai-Long Wu,et al. Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data. , 2009, Talanta.
[25] A. Zhang,et al. Studies of 3D-quantitative structure-activity relationships on a set of nitroaromatic compounds: CoMFA, advanced CoMFA and CoMSIA. , 2002, Chemosphere.
[26] M. J. Duart,et al. Use of QSAR methods for predicting the chemiluminescent behaviour of organic compounds upon reaction with potassium permanganate in an acid medium. , 2009, Talanta.
[27] L. Buydens,et al. Multivariate calibration with least-squares support vector machines. , 2004, Analytical chemistry.
[28] C. Supuran,et al. Carbonic anhydrases: current state of the art, therapeutic applications and future prospects. , 2004, Journal of enzyme inhibition and medicinal chemistry.
[29] C. Supuran,et al. Modulation of carbonic anhydrase activity and its applications in therapy , 2004 .