Predicting the protein localization sites using artificial neural networks
© Arulmozhi and Reghunadhan; licensee BioMed Central Ltd. 2013
Published: 22 March 2013
Chemoinformatics, the brain child of Frank Brown , has now evolved into a new branch of science, which has high correlations with computer science, bioinformatics, and chemistry. The major functionalities of Chemoinformatics include, but not limited to, chemical structure/property prediction, molecular similarity/diversity analysis, virtual screening, qualitative/quantitative structural/activity/property relationship, design of combinatorial libraries, statistical models, descriptors, drug discovery, representation of chemical compounds/reactions, classification/search/storage methods, management of compound databases, high-throughput docking, data analysis methods, etc. This paper deals with the prediction of localization sites of protein using neural network.
Neural Network  provides learning capability and it is one of the important components of softcomputing. A neural network will consist of one input layer, one or more number of hidden layers and an output layer. Number of neurons in the input layer will be equal to the number of features passed to the neural network. Number of neurons in the output layer will be equal to the number of classes for classification purpose. Hidden neurons are usually fixed by experts depending on the problem. There are various types of neural network available like feedforward neural networks, feedback networks, reccurrent networks, self organizing maps, anfis, etc.
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