Reduction strategies for hierarchical multi-label classification in protein function prediction

BMC Bioinformatics

Methodology Article

Ricardo Cerri¹  Yaochu Jin²  Rodrigo C. Barros³  André C. P. L. F. de Carvalho⁴ 

[1] Department of Computer Science, UFSCar Federal University of São Carlos, Rodovia Washington Luís, Km 235, 13565-905, São Carlos, SP, Brazil;Department of Computer Science, University of Surrey, GU2 7XH Guildford, Surrey, United Kingdom;Faculdade de Informática, Pontifícia Universidade Católica do Rio Grande do Sul, Av. Ipiranga, 6681, 90619-900, Porto Alegre, RS, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Campus de São Carlos 135, 13566-590, São Carlos, SP, Brazil;

关键词: Hierarchical multi-label classification;    Protein function prediction;    Machine learning;    Neural networks;   

DOI  :  10.1186/s12859-016-1232-1

 received in 2016-01-16, accepted in 2016-08-30,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundHierarchical Multi-Label Classification is a classification task where the classes to be predicted are hierarchically organized. Each instance can be assigned to classes belonging to more than one path in the hierarchy. This scenario is typically found in protein function prediction, considering that each protein may perform many functions, which can be further specialized into sub-functions. We present a new hierarchical multi-label classification method based on multiple neural networks for the task of protein function prediction. A set of neural networks are incrementally training, each being responsible for the prediction of the classes belonging to a given level.ResultsThe method proposed here is an extension of our previous work. Here we use the neural network output of a level to complement the feature vectors used as input to train the neural network in the next level. We experimentally compare this novel method with several other reduction strategies, showing that it obtains the best predictive performance. Empirical results also show that the proposed method achieves better or comparable predictive performance when compared with state-of-the-art methods for hierarchical multi-label classification in the context of protein function prediction.ConclusionsThe experiments showed that using the output in one level as input to the next level contributed to better classification results. We believe the method was able to learn the relationships between the protein functions during training, and this information was useful for classification. We also identified in which functional classes our method performed better.

【 授权许可】

CC BY   
© The Author(s) 2016

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