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Critical evaluation of web-based prediction tools for human protein subcellular localization
- Source :
- Briefings in Bioinformatics. 21:1628-1640
- Publication Year :
- 2019
- Publisher :
- Oxford University Press (OUP), 2019.
-
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/.
- Subjects :
- Web server
Computer science
0206 medical engineering
Datasets as Topic
02 engineering and technology
computer.software_genre
Machine learning
Set (abstract data type)
03 medical and health sciences
Humans
Web application
Molecular Biology
030304 developmental biology
Multi-label classification
Internet
0303 health sciences
business.industry
Proteins
Construct (python library)
Protein subcellular localization prediction
Data set
Benchmarking
Artificial intelligence
UniProt
business
computer
020602 bioinformatics
Subcellular Fractions
Information Systems
Subjects
Details
- ISSN :
- 14774054 and 14675463
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Briefings in Bioinformatics
- Accession number :
- edsair.doi.dedup.....2ac50bad38f80100edea1619c5bf3f72