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Authors: Kenji Satou 1 ; Yoshiki Shimaguchi 2 ; Kunti Mahmudah 2 ; Ngoc Nguyen 2 ; Mera Delimayanti 3 ; Bedy Purnama 4 ; Mamoru Kubo 1 ; Makiko Kakikawa 1 and Yoichi Yamada 1

Affiliations: 1 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan ; 2 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan ; 3 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan, Department of Computer and Informatics Engineering, Politeknik Negeri Jakarta, Jakarta, Indonesia ; 4 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan, Telkom School of Computing, TELKOM University, Bandung, Indonesia

ISBN: 978-989-758-353-7

Keyword(s): Nuclear Protein, Subnuclear Location, Deep Learning, Feature Selection.

Abstract: To play a biomolecular function, a protein must be transported to a specific location of cell. Also in a nucleus, a nuclear protein has its own location to fulfil its role. In this study, subnuclear location of nuclear protein was predicted from protein sequence by using deep learning algorithm. As a dataset for experiments, 319 non-homologous protein sequences with class labels corresponding to 13 classes of subcellular localization (e.g. "Nuclear envelope") were selected from public databases. In order to achieve better performance, various combinations of feature generation methods, classification algorithms, parameter tuning, and feature selection were tested. Among 17 methods for generating features of protein sequences, Composition/Transition/Distribution (CTD) generated the most effective features. They were further selected by randomForest package for R. Using the selected features, quite high accuracy (99.91%) was achieved by a deep neural network with seven hidden layers, ma xout activation function, and RMSprop optimization algorithm. (More)

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Paper citation in several formats:
Satou, K.; Shimaguchi, Y.; Mahmudah, K.; Nguyen, N.; Delimayanti, M.; Purnama, B.; Kubo, M.; Kakikawa, M. and Yamada, Y. (2019). Prediction of Subnuclear Location for Nuclear Protein.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-353-7, pages 276-280. DOI: 10.5220/0007570502760280

@conference{bioinformatics19,
author={Kenji Satou. and Yoshiki Shimaguchi. and Kunti Robiatul Mahmudah. and Ngoc Giang Nguyen. and Mera Kartika Delimayanti. and Bedy Purnama. and Mamoru Kubo. and Makiko Kakikawa. and Yoichi Yamada.},
title={Prediction of Subnuclear Location for Nuclear Protein},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={276-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007570502760280},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Prediction of Subnuclear Location for Nuclear Protein
SN - 978-989-758-353-7
AU - Satou, K.
AU - Shimaguchi, Y.
AU - Mahmudah, K.
AU - Nguyen, N.
AU - Delimayanti, M.
AU - Purnama, B.
AU - Kubo, M.
AU - Kakikawa, M.
AU - Yamada, Y.
PY - 2019
SP - 276
EP - 280
DO - 10.5220/0007570502760280

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