Progress in Machine Learning-based Predicting Subcellular Localizations of Proteins with Multiple Sites

DOI

10.2991/csss-14.2014.85How to use a DOI?

Keywords

protein subcellular localization; dataset; feature extraction; predicting algorithm; validation test

Abstract

Prediction of protein subcellular localizations is a key step to determinate the functions of proteins. The experimental methods are both expensive and time-consuming. Therefore, many machine learning based computational approaches were proposed in the last two decades. Recently, it is proved that the number of proteins with multiple sites is rising. To determinate the subcellular localizations of this kind of proteins is a more difficult problem. Generally, dataset construction, feature representation, algorithm design and validation test are the four main aspects need to be considered in developing the predicting algorithms. This paper reviewed these four topics in detail. Although a great success has been got by many researchers, there are still a lot of problems need to study deeply.

Copyright

© 2014, 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  - Qiao Shanping
PY  - 2014/06
DA  - 2014/06
TI  - Progress in Machine Learning-based Predicting Subcellular Localizations of Proteins with Multiple Sites
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 362
EP  - 365
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.85
DO  - 10.2991/csss-14.2014.85
ID  - Shanping2014/06
ER  -