A Novel Local And Global Similarity Based Feature...

A Novel Local And Global Similarity Based Feature...

A Novel Local and Global Similarity based Feature

Extraction Approach for Protein Classification

Abstract–In this article, a novel approach is proposed based on local and global similarity for extracting features from protein sequences. The proposed approach extract only 6 features corresponding to each protein sequence. These features are computed by globally considering the probabilities of occurrences of the amino acids in different position of the sequences which locally belongs to the six exchange groups [1]. Then, these features are used as an inputs for Neural Network learning algorithm named as Boolean–Like Training Algorithm (BLTA) [2]. The

BLTA classifier is used to classify the protein sequences obtained from the Protein Information Resource (PIR) maintained by the

National Biomedical Research Foundation (NBREF–PIR) [3]. To investigate the efficacy of proposed feature extraction approach, the experimentation is performed on two superfamilies, namely

Ras and Globin. Across tenfold cross validation, the highest

Classification Accuracy and Computational Time achieved by proposed approach is 94.323.52 and 6.54(s) respectively in comparison to the Classification Accuracies achieved by other approaches

[4], [5] and [6] are 85.420.55, 67.518.38, 51.410.27 with Computational Time 7.11(s), 10.13(s), 63.98(s) respectively.

The experimental results demonstrate that the proposed approach extract the minimum relevant features for each protein sequence.

Therefore, it

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