NDLI: iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins

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Molecular BioSystems Vol - 9 Issue - 3

Molecular BioSystems Vol - 9 Issue - 2

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Molecular BioSystems Vol -6

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Molecular BioSystems Vol -4

Molecular BioSystems Vol -3

Molecular BioSystems Vol -2

iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins

Content Provider	Royal Society of Chemistry (RSC)
Author	Chou, Kuo-Chen Xiao, Xuan Fang, Jian-An Lin, Wei-Zhong
Copyright Year	2013
Abstract	Predicting protein subcellular localization is a challenging problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multi-label proteins should not be ignored because they usually bear some special function worthy of in-depth studies. By introducing the “multi-label learning” approach, a new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure the prediction quality of a multi-label system in a rigorous way, five indices were introduced; they are “Absolute-True”, “Absolute-False” (or Hamming-Loss”), “Accuracy”, “Precision”, and “Recall”. As a demonstration, the jackknife cross-validation was performed with iLoc-Animal on a benchmark dataset of animal proteins classified into the following 20 location sites: (1) acrosome, (2) cell membrane, (3) centriole, (4) centrosome, (5) cell cortex, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracellular, (11) Golgi apparatus, (12) lysosome, (13) mitochondrion, (14) melanosome, (15) microsome, (16) nucleus, (17) peroxisome, (18) plasma membrane, (19) spindle, and (20) synapse, where many proteins belong to two or more locations. For such a complicated system, the outcomes achieved by iLoc-Animal for all the aforementioned five indices were quite encouraging, indicating that the predictor may become a useful tool in this area. It has not escaped our notice that the multi-label approach and the rigorous measurement metrics can also be used to investigate many other multi-label problems in molecular biology. As a user-friendly web-server, iLoc-Animal is freely accessible to the public at the web-site http://www.jci-bioinfo.cn/iLoc-Animal.
Starting Page	634
Ending Page	644
Page Count	11
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	9
Issue Number	4
Journal	Molecular BioSystems
DOI	10.1039/c3mb25466f
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Cross-validation (statistics) Cell membrane Cell cortex Lysosome Microsome Protein Golgi apparatus Mitochondrion Synapse Animal Peroxisome Cytoskeleton Jackknife resampling Special functions Melanosome Acrosome Endoplasmic reticulum Endosome Centriole Centrosome Cytoplasm
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins

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iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins