Prediction of Subnuclear Location for Nuclear Protein

Kenji Satou, Yoshiki Shimaguchi, Kunti Robiatul Mahmudah, Ngoc Giang Nguyen, Mera Kartika Delimayanti, Mera Kartika Delimayanti, Bedy Purnama, Bedy Purnama, Mamoru Kubo, Makiko Kakikawa, Yoichi Yamada

2019

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, maxout activation function, and RMSprop optimization algorithm.

Paper Citation

in Harvard Style

Satou K., Shimaguchi Y., Mahmudah K., Nguyen N., Kartika 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 (BIOSTEC 2019) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-353-7, SciTePress, pages 276-280. DOI: 10.5220/0007570502760280

in Bibtex Style

@conference{bioinformatics19,
author={Kenji Satou and Yoshiki Shimaguchi and Kunti Robiatul Mahmudah and Ngoc Giang Nguyen and Mera Kartika 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 (BIOSTEC 2019) - Volume 3: BIOINFORMATICS},
year={2019},
pages={276-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007570502760280},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - 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 - Kartika Delimayanti M.
AU - Purnama B.
AU - Kubo M.
AU - Kakikawa M.
AU - Yamada Y.
PY - 2019
SP - 276
EP - 280
DO - 10.5220/0007570502760280
PB - SciTePress