Abstract
Classifying an unknown protein into a known protein family is a challenging task and one of the demanding problems is bioinformatics and computational biology. As proteins are the main target molecules while designing the drug for any disease, it is important to study and classify unknown proteins. Machine learning algorithms like support vector machines, decision tree classifier, naïve Bayes classifier, and artificial neural networks have been effectively used for such kind of problems. In this paper, our aim is to classify an unknown protein sequence into known protein family using machine learning algorithms and to compare their performance. Here, the protein feature used for classification purpose is the probability of occurrence of a particular amino acid in the protein sequence. There are mainly 20 amino acids which form a protein. The idea here is proteins having nearly similar probability of occurrence of amino acids belong to the same family.
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Satpute, B.S., Yadav, R. (2019). An Efficient Machine Learning Technique for Protein Classification Using Probabilistic Approach. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_41
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DOI: https://doi.org/10.1007/978-981-13-1610-4_41
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