pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC-Reference-Cited by-同舟云学术

pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC

Published:2017 Issue:9 Volume:13 Page:1722-1727
ISSN:1742-206X
Container-title:Molecular BioSystems
language:en
Short-container-title:Mol. BioSyst.

Author:

Cheng Xiang¹²³⁴,Xiao Xuan¹²³⁴⁵,Chou Kuo-Chen⁵⁶⁷⁸⁹

Affiliation:

1. Computer Department

2. Jingdezhen Ceramic Institute

3. Jingdezhen

4. China

5. The Gordon Life Science Institute

6. Boston

7. USA

8. Center for Informational Biology

9. University of Electronic Science and Technology of China

Abstract

One of the fundamental goals in cellular biochemistry is to identify the functions of proteins in the context of compartments that organize them in the cellular environment.

Publisher

Royal Society of Chemistry (RSC)

Subject

Molecular Biology,Biotechnology

Link

http://pubs.rsc.org/en/content/articlepdf/2017/MB/C7MB00267J

Reference65 articles.

1. Protein sorting signals and prediction of subcellular localization

2. Recent progress in protein subcellular location prediction

3. Automated Subcellular Location Determination and High-Throughput Microscopy

4. Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

5. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites

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