scholarly journals Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features

2016 ◽  
pp. btw723 ◽  
Author(s):  
Hang Zhou ◽  
Yang Yang ◽  
Hong-Bin Shen
Keyword(s):  
Gene Ontology ◽  
Subcellular Localization ◽  
Human Protein ◽  
Functional Domain ◽  
Protein Subcellular Localization ◽  
Hidden Correlations

scholarly journals HomoKinase: A Curated Database of Human Protein Kinases

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Suresh Subramani ◽  
Saranya Jayapalan ◽  
Raja Kalpana ◽  
Jeyakumar Natarajan
Keyword(s):  
Gene Ontology ◽  
Protein Kinases ◽  
Kinase Activity ◽  
Query Protein ◽  
Amino Acid Sequences ◽  
Human Protein ◽  
Biological Information ◽  
Functional Domain ◽  
Comprehensive Collection ◽  
Search Tool

HomoKinase database is a comprehensive collection of curated human protein kinases and their relevant biological information. The entries in the database are curated by three criteria: HGNC approval, gene ontology-based biological process (protein phosphorylation), and molecular function (ATP binding and kinase activity). For a given query protein kinase name, the database provides its official symbol, full name, other known aliases, amino acid sequences, functional domain, gene ontology, pathways assignments, and drug compounds. In addition, as a search tool, it enables the retrieval of similar protein kinases with specific family, subfamily, group, and domain combinations and tabulates the information. The present version contains 498 curated human protein kinases and links to other popular databases.

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

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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