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Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
Joint Authors
Wu, Jian-Sheng
Hu, Hai-Feng
Yan, Shan-Cheng
Tang, Li-Hua
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-05-05
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Jian-Sheng& Hu, Hai-Feng& Yan, Shan-Cheng& Tang, Li-Hua. 2015. Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens. BioMed Research International،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1056154
Modern Language Association (MLA)
Wu, Jian-Sheng…[et al.]. Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens. BioMed Research International No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1056154
American Medical Association (AMA)
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. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1056154
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1056154