Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106993
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Wan, S | - |
dc.creator | Mak, MW | - |
dc.creator | Kung, SY | - |
dc.date.accessioned | 2024-06-07T00:59:29Z | - |
dc.date.available | 2024-06-07T00:59:29Z | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10397/106993 | - |
dc.language.iso | en | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | © The Author 2016. Published by Oxford University Press. All rights reserved. | en_US |
dc.rights | This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Shibiao Wan, Man-Wai Mak, Sun-Yuan Kung, FUEL-mLoc: feature-unified prediction and explanation of multi-localization of cellular proteins in multiple organisms, Bioinformatics, Volume 33, Issue 5, March 2017, Pages 749–750 is available online at: https://doi.org/10.1093/bioinformatics/btw717. | en_US |
dc.title | FUEL-mLoc : feature-unified prediction and explanation of multi-localization of cellular proteins in multiple organisms | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 749 | - |
dc.identifier.epage | 750 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 5 | - |
dc.identifier.doi | 10.1093/bioinformatics/btw717 | - |
dcterms.abstract | Although many web-servers for predicting protein subcellular localization have been developed, they often have the following drawbacks: (i) lack of interpretability or interpreting results with heterogenous information which may confuse users; (ii) ignoring multi-location proteins and (iii) only focusing on specific organism. To tackle these problems, we present an interpretable and efficient web-server, namely FUEL-mLoc, using Feature-Unified prediction and Explanation of multi-Localization of cellular proteins in multiple organisms. Compared to conventional localization predictors, FUEL-mLoc has the following advantages: (i) using unified features (i.e. essential GO terms) to interpret why a prediction is made; (ii) being capable of predicting both single- and multi-location proteins and (iii) being able to handle proteins of multiple organisms, including Eukaryota, Homo sapiens, Viridiplantae, Gram-positive Bacteria, Gram-negative Bacteria and Virus. Experimental results demonstrate that FUEL-mLoc outperforms state-of-the-art subcellular-localization predictors. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Bioinformatics, Mar. 2017, v. 33, no. 5, p. 749-750 | - |
dcterms.isPartOf | Bioinformatics | - |
dcterms.issued | 2017-03 | - |
dc.identifier.scopus | 2-s2.0-85020069484 | - |
dc.identifier.eissn | 1367-4811 | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0769 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6750344 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mak_Fuel-Mloc_Feature-Unified_Prediction.pdf | Pre-Published version | 848.78 kB | Adobe PDF | View/Open |
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