iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins

By Kuo-Chen Chou, Zhi-cheng Wu, Xuan Xiao. 2011

322 citations recorded by the source corpus.

Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or “singleplex” proteins. Actually, multiple-location or “multiplex” proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the “multi-labeled learning” and “accumulation-layer scale”, a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.

Subfields: Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Subcellular localization prediction was trained and evaluated using the DeepLoc data set [47] for which performance was measured for several methods, namely: LocTree2 [72], MultiLoc2 [73], Sher-Loc2 [74], CELLO [75], iLoc-Euk [52], WoLF PSORT [76] and YLoc [77].”
Impacts of bioinformatics to medicinal chemistry. (2015, unknown): “a See [260].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction 3 was trained and evaluated using the DeepLoc data set [46] for which performance was 4 measured for several methods, namely: LocTree2 [66], MultiLoc2 [67], SherLoc2 [68], CELLO 5 [69], iLoc-Euk [51], WoLF PSORT [70] and YLoc [71].”
iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, background): “Because some AMPs may belong to two or more functional attributes [6,7], it is instructive to introduce the concept of ‘‘virtual AMP’’ as done in [16,17] when dealing with proteins with multiple location sites.”
A Tutorial on Multilabel Learning (2015, methodology): “—Economic activities classification [Ciarelli et al. 2009]: The task of finding a correspondence between a contract that contains a description of the business activities of one company and a set of standard categories. —Patent classification: Because a patent document may be associated with several”