Hierarchical multilabel classifier for gene ontology annotation using multihead and multiend deep CNN model. Issue 7 (4th May 2020)

• Record Type: Journal Article
• Authors: Yuan, Xin; Pang, Erli; Lin, Kui; Hu, Jinglu
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 15:Issue 7(2020)
• Page Start: 1057
• Page End: 1064
• Publication Date: 2020-05-04
• Subjects: hierarchical multilabel classification -- GO annotation -- deep learning -- autoencoder -- feature extraction; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23150
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract: Gene ontology annotation is known to be a very complicated multilabel classification task, and the hierarchical multilabel classification (HMC) approaches with local classifiers have been shown to be effective for the task. In a traditional HMC method, a set of hierarchically organized simple local classifiers are usually used, each of which for one hierarchical level separately. In this paper, we propose a novel hierarchical multilabel classifier implementing the whole set of hierarchically organized local classifiers in one deep convolution neural network (CNN) model with multiple heads and multiple ends (MHME). The proposed MHME CNN model consists of three parts: the body part of a deep CNN model shared by different local classifiers for feature extraction and feature mapping; the multiend part of a set of autoencoders performing feature fusion transforming the input vectors of different local classifiers to feature vectors with the same length so as to share the feature mapping part; and the multihead part of a set of linear multilabel classifiers. Furthermore, a sophisticated recursive algorithm is designed to train the MHME CNN model to realize the functions of a set of hierarchically organized local classifiers. In this way, by sharing a deep CNN with multiple local classifiers, we are able to construct more powerful local classifiers for each level with limited training samples, and to achieve better classification performance. Experiment results on various benchmark datasets show that the proposed deep CNN‐based model has better performance than the state‐of‐the‐art traditional models. Moreover, it gives rather good performance even under a transfer learning. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.