Neural networks and support vector machines based bio-activity classification

Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM

Flood. An open source neural networks C++ library

The multilayer perceptron is an important model of neural network, and much of the literature in the eld is referred to that model. The multilayer perceptron has found a wide range of applications, which include function re- gression, pattern recognition, time series prediction, optimal control, optimal shape design or inverse problems. All these problems can be formulated as variational problems. That neural network can learn either from databases or from mathematical models. Flood is a comprehensive class library which implements the multilayer perceptron in the C++ programming language. It has been developed follow- ing the functional analysis and calculus of variations theories. In this regard, this software tool can be used for the whole range of applications mentioned above. Flood also provides a workaround for the solution of function opti- mization problems

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Representation of Functional Data in Neural Networks

Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.Comment: Also available online from: http://www.sciencedirect.com/science/journal/0925231