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The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI(More)
The wavelet neural network (WNN) was used to predict the programmed-temperature retention values of naphthas. In WNN, a Morlet mother wavelet was used as a transfer function, and the convergence speed was faster than other neural networks. Sixty-four compounds (selected randomly from 94) were used as a training set, and the 30 remaining compounds were used(More)
Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data(More)
The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear(More)
The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional,(More)
The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as(More)
The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function(More)
The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the first time. The heuristic method was used to search the descriptor space and select the descriptors responsible for activity. A nonlinear,(More)
The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features(More)
A new method support vector machine (SVM) and the heuristic method (HM) were used to develop the nonlinear and linear models between the capacity factor (logk) and seven molecular descriptors of 75 peptides for the first time. The molecular descriptors representing the structural features of the compounds only included the constitutional and topological(More)