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• W1527957158 abstract "The protein superfamily classification problem, which consists of determining the superfamily membership of a given unknown protein sequence, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular function and medical diagnosis. In this work, we propose a new approach for protein classification based on a Probabilistic Neural Network and feature selection. Our goal is to predict the functional family of novel protein sequences based on the features extracted from the protein’s primary structure i.e., sequence only. For this purpose, the datasets are extracted form Protein Data Bank(PDB), a curated protein family database, are used as training datasets. In these conducted experiments, the performance of the classifier is compared to other known data mining approaches / sequence comparison methods. The computational results have shown that the proposed method performs better than the other ones and looks promising for problems with characteristics similar to the problem." @default.
• W1527957158 created "2016-06-24" @default.
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• W1527957158 date "2009-01-01" @default.
• W1527957158 modified "2023-09-27" @default.
• W1527957158 title "A PROBABILISTIC NEURAL NETWORK APPROACH FOR PROTEIN SUPERFAMILY CLASSIFICATION" @default.
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