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Quantitative structure-activity relationship (QSAR) studies based on a data set of 88 phenylalkylamines has been implemented. These chemicals used are among the most widely abused hallucinogens especially for young people. Because of the difficulty of assaying hallucinogenic activities, it is particularly important to develop predictive models. In this work, quantitative structure-activity relationships...
Influenza viruses continue to evolve rapidly and are responsible for seasonal epidemics and occasional, but catastrophic, pandemics. We recently demonstrated the use of decision tree and support vector machine methods in classifying pandemic swine flu viral strains with high accuracy. Here, we applied the technique of artificial neural networks for the prediction of important influenza virus antigenic...
Cancer is a group of complex diseases, in which a relatively large number of genes are involved. One of the main goals of cancer research is to identify genes that causally relevant to the development and progress of cancer. The increasingly identified cancer genes and availability of genomic and proteomics data provide us opportunities to identify cancer genes by computational methods. In this work,...
Human mitochondrial proteins are involved in fundamental biological process including apoptosis, energy production and many metabolic pathways, prediction of mitochondrial proteins is a major challenge in genome annotation. In this study, we implemented a machine learning approach and developed reliable neural network and SVM based methods to classify human mitochondria proteins with high confidence...
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