Ekstraksi Fitur Rantai Markov untuk Klasifikasi Famili Protein

Toto Haryanto, Rizky Kurniawan, Sony Muhammad, Aziz Kustiyo, Endang Purnama Giri

Abstract


As complex molecules, proteins have various roles for living things. Proteins are organic molecules formed from twenty amino acid combinations with various functions for living things, such as transportation systems, a catalyst of chemical reactions for metabolism, and food reserves. This research aims to classify proteins family based on sequences of amino acids as the primary structure. There are 300 amino acid fragments obtained from the Pfam database. The proteins family database subset with three sub-sample classes was obtained, including 1-cysPrx_C, 4HBT, and ABC_Tran. In this research, the first and second order of the Markov chain for extracting features were applied. Moreover, we use a Probabilistic Neural Network (PNN) as a classifier compared to the joint probability technique with Markov assumptions. We evaluate the results by comparing the sensitivity and specificity of both classification techniques. The evaluation results show that overall, PNN has slightly better performance than the joint probability technique for classifying protein families.

Keywords


amino acid, joint probability, markov chain, probabilistic neural network, protein family

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References


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DOI: http://dx.doi.org/10.30646/sinus.v21i2.748

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