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Predicting the subcellular localization of proteins with multiple sites based on multiple features fusion

Content Provider	ACM Digital Library
Author	Zhao, Qing Wang, Dong Chen, Yuehui Qu, Xumi Qiao, Shanping
Copyright Year	2016
Abstract	Protein sub-cellular localization prediction has attracted much attention in recent years because of its importance for protein function studying and targeted drug discovery, and that makes it to be an important research field in bioinformatics. Traditional experimental methods which ascertain the protein sub-cellular locations are costly and time consuming. In the last two decades, machine learning methods got increasing development, and a large number of machine learning based protein sub-cellular location predictors have been developed. However, most of such predictors can only predict proteins in only one subcellular location. With the development of biology techniques, more and more proteins which have two or even more sub-cellular locations have been found. It is much more significant to study such proteins because they have extremely useful implication for both basic biology and bioinformatics research. In order to improve the accuracy of prediction, much more feature information which can represent the protein sequence should be extracted. In this paper, several feature extraction methods were fused together to extract the feature information, then the multi-label $\textit{k}$ nearest neighbors (ML-KNN) algorithm was used to predict protein sub-cellular locations. The best overall accuracies we got for dataset s1 in constructing Gpos-mploc is 66.7304 and 59.9206 percent for dataset s2 in constructing Virus-mPLoc.
Starting Page	36
Ending Page	42
Page Count	7
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2015.2485207
Volume Number	13
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	N-terminal signals Amino acid index distribution Multi-label $\textit{k}$ nearest neighbor Physicochemical properties Pseudo amino acid composition
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Predicting the Subcellular Localization of Proteins with Multiple Sites Based on Multiple Features Fusion

Predicting the Subcellular Localization of Proteins with Multiple Sites Based on N-Terminal Signals

A novel feature fusion method for predicting protein subcellular localization with multiple sites

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets.

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets

MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition

Prediction of Membrane Protein Types Using Pseudo-Amino Acid Composition and Ensemble Classification

Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins

BLKnn: A K-nearest neighbors method for predicting bioluminescent proteins

Predicting the subcellular localization of proteins with multiple sites based on multiple features fusion

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