scholarly journals Correction: Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
Author(s):  
Suyu Mei
Keyword(s):  
Subcellular Localization ◽  
Transfer Learning ◽  
Human Protein ◽  
Protein Subcellular Localization

Prediction of human protein subcellular localization using deep learning

2018 ◽  
Vol 117 ◽  
pp. 212-217 ◽  
Author(s):  
Leyi Wei ◽  
Yijie Ding ◽  
Ran Su ◽  
Jijun Tang ◽  
Quan Zou
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Human Protein ◽  
Protein Subcellular Localization

scholarly journals Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiaotong Guo ◽  
Fulin Liu ◽  
Ying Ju ◽  
Zhen Wang ◽  
Chunyu Wang
Keyword(s):  
Subcellular Localization ◽  
Ensemble Classifier ◽  
Human Protein ◽  
Protein Subcellular Localization

Critical evaluation of web-based prediction tools for human protein subcellular localization

2019 ◽  
Vol 21 (5) ◽  
pp. 1628-1640 ◽  
Author(s):  
Yinan Shen ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Quan Zou ◽  
Fei Guo
Keyword(s):  
Subcellular Localization ◽  
Critical Evaluation ◽  
Human Protein ◽  
Data Sets ◽  
Protein Subcellular Localization ◽  
Data Set ◽  
Prediction Tools ◽  
Benchmark Data ◽  
Subcellular Localization Prediction ◽  
Localization Prediction

Abstract Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/.

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