Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/80041
Title: | Ensemble positive unlabeled learning for disease gene identification | Authors: | Yang, Peng Li, Xiaoli Chua, Hon-Nian Kwoh, Chee-Keong Ng, See-Kiong |
Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2014 | Source: | Yang, P., Li, X., Chua, H.-N., Kwoh, C.-K.,& Ng, S.-K. (2014). Ensemble Positive Unlabeled Learning for Disease Gene Identification. PLoS ONE, 9(5), e97079-. | Series/Report no.: | PLoS ONE | Abstract: | An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions. | URI: | https://hdl.handle.net/10356/80041 http://hdl.handle.net/10220/19767 |
ISSN: | 1932-6203 | DOI: | 10.1371/journal.pone.0097079 | Schools: | School of Computer Engineering | Research Centres: | Bioinformatics Research Centre | Rights: | © 2014 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Ensemble Positive Unlabeled Learning for Disease Gene Identification.pdf | 706.46 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
5
81
Updated on Mar 18, 2024
Web of ScienceTM
Citations
5
56
Updated on Oct 24, 2023
Page view(s) 20
753
Updated on Mar 18, 2024
Download(s) 20
242
Updated on Mar 18, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.