A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks

DOI

10.2991/acaai-18.2018.58How to use a DOI?

Keywords

LSTM; multi-label classification; protein subcellular localization

Abstract

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory (LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhiying Gao
AU  - Lijun Sun
AU  - Zhihua Wei
PY  - 2018/03
DA  - 2018/03
TI  - A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks
BT  - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)
PB  - Atlantis Press
SP  - 248
EP  - 252
SN  - 1951-6851
UR  - https://doi.org/10.2991/acaai-18.2018.58
DO  - 10.2991/acaai-18.2018.58
ID  - Gao2018/03
ER  -