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Reduction Strategies for Hierarchical Multi-Label Classification in Protein Function Prediction

Cerri, R, Barros, RC, de Carvalho, ACPLF and Jin, Y (2016) Reduction Strategies for Hierarchical Multi-Label Classification in Protein Function Prediction BMC Bioinformatics, 17 (373).

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Abstract

Background Hierarchical 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. Results The 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. Conclusions The 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.

Item Type: Article
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Cerri, RUNSPECIFIEDUNSPECIFIED
Barros, RCUNSPECIFIEDUNSPECIFIED
de Carvalho, ACPLFUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 15 September 2016
Identification Number : https://doi.org/10.1186/s12859-016-1232-1
Copyright Disclaimer : © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Uncontrolled Keywords : Hierarchical multi-label classification, Protein function prediction, Machine learning, Neural networks
Depositing User : Symplectic Elements
Date Deposited : 03 Oct 2016 09:43
Last Modified : 03 Oct 2016 09:43
URI: http://epubs.surrey.ac.uk/id/eprint/812320

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