Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens

Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated p...

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Main Authors:	Wu, Jian-Sheng, Hu, Hai-Feng, Yan, Shan-Cheng, Tang, Li-Hua
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436452/

id	pubmed-4436452
recordtype	oai_dc
spelling	pubmed-44364522015-06-14 Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens Wu, Jian-Sheng Hu, Hai-Feng Yan, Shan-Cheng Tang, Li-Hua Research Article Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from weak-label problem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation. Hindawi Publishing Corporation 2015 2015-05-05 /pmc/articles/PMC4436452/ /pubmed/26075251 http://dx.doi.org/10.1155/2015/619438 Text en Copyright © 2015 Jian-Sheng Wu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wu, Jian-Sheng Hu, Hai-Feng Yan, Shan-Cheng Tang, Li-Hua
spellingShingle	Wu, Jian-Sheng Hu, Hai-Feng Yan, Shan-Cheng Tang, Li-Hua Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
author_facet	Wu, Jian-Sheng Hu, Hai-Feng Yan, Shan-Cheng Tang, Li-Hua
author_sort	Wu, Jian-Sheng
title	Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
title_short	Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
title_full	Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
title_fullStr	Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
title_full_unstemmed	Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
title_sort	multi-instance multilabel learning with weak-label for predicting protein function in electricigens
description	Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from weak-label problem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.
publisher	Hindawi Publishing Corporation
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436452/
_version_	1613225246761943040

Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens

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