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BioMed Research International
Volume 2015 (2015), Article ID 619438, 9 pages
http://dx.doi.org/10.1155/2015/619438
Research Article

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

1School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210046, China
2School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210046, China

Received 1 October 2014; Accepted 16 December 2014

Academic Editor: Yuedong Yang

Copyright © 2015 Jian-Sheng Wu et al. 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.

Abstract

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.