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Authors: Neha Bharill and Aruna Tiwari

Affiliation: Indian Institute of Technology, India

ISBN: 978-989-758-070-3

Keyword(s): Bioinformatics, Probability-based Features, Position-specific Information, Binary Feed Forward Neural Network, Protein Classification.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Databases and Data Management ; Genomics and Proteomics ; Pattern Recognition, Clustering and Classification ; Sequence Analysis ; Structural Bioinformatics

Abstract: The paper aims to propose a novel approach for extracting features from protein sequences. This approach extracts only 6 features for each protein sequence which are computed by globally considering the probabilities of occurrences of the amino acids in different position of the sequences within the superfamily which locally belongs to the six exchange groups. Then, these features are used as an input for Neural Network learning algorithm named as Boolean-Like Training Algorithm (BLTA). The BLTA classifier is used to classify the protein sequences obtained from the Protein Information Resource (PIR). 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 achieved by proposed approach is 94.32±3.52 with Computational Time 6.54±0.10 (s) is remarkably better in comparison to the Classification Accuracies achieved by other approa ches. The experimental results demonstrate that the proposed approach extracts the minimum number of features for each protein sequence. Therefore, it results in considerably potential improvement in Classification Accuracy and takes less Computational Time for protein sequence classification in comparison with other well-known feature extraction approaches. (More)

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Paper citation in several formats:
Bharill, N. and Tiwari, A. (2015). A Novel Technique of Feature Extraction Based on Local and Global Similarity Measure for Protein Classification.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015) ISBN 978-989-758-070-3, pages 219-224. DOI: 10.5220/0005283702190224

@conference{bioinformatics15,
author={Neha Bharill. and Aruna Tiwari.},
title={A Novel Technique of Feature Extraction Based on Local and Global Similarity Measure for Protein Classification},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)},
year={2015},
pages={219-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005283702190224},
isbn={978-989-758-070-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)
TI - A Novel Technique of Feature Extraction Based on Local and Global Similarity Measure for Protein Classification
SN - 978-989-758-070-3
AU - Bharill, N.
AU - Tiwari, A.
PY - 2015
SP - 219
EP - 224
DO - 10.5220/0005283702190224

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