Ensemble positive unlabeled learning for disease gene identification

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Yang, Peng, Li, Xiaoli, Chua, Hon-Nian, Kwoh, Chee-Keong, Ng, See-Kiong
Other Authors: Hernandez-Lemus, Enrique
Format: Article
Language:English
Published: 2014
Subjects:
Online Access:https://hdl.handle.net/10356/80041
http://hdl.handle.net/10220/19767
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-80041
record_format dspace
spelling sg-ntu-dr.10356-800412022-02-16T16:28:28Z Ensemble positive unlabeled learning for disease gene identification Yang, Peng Li, Xiaoli Chua, Hon-Nian Kwoh, Chee-Keong Ng, See-Kiong Hernandez-Lemus, Enrique School of Computer Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering 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. Published version 2014-06-13T07:54:56Z 2019-12-06T13:39:17Z 2014-06-13T07:54:56Z 2019-12-06T13:39:17Z 2014 2014 Journal Article 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-. 1932-6203 https://hdl.handle.net/10356/80041 http://hdl.handle.net/10220/19767 10.1371/journal.pone.0097079 24816822 en PLoS ONE © 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. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Yang, Peng
Li, Xiaoli
Chua, Hon-Nian
Kwoh, Chee-Keong
Ng, See-Kiong
Ensemble positive unlabeled learning for disease gene identification
description 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.
author2 Hernandez-Lemus, Enrique
author_facet Hernandez-Lemus, Enrique
Yang, Peng
Li, Xiaoli
Chua, Hon-Nian
Kwoh, Chee-Keong
Ng, See-Kiong
format Article
author Yang, Peng
Li, Xiaoli
Chua, Hon-Nian
Kwoh, Chee-Keong
Ng, See-Kiong
author_sort Yang, Peng
title Ensemble positive unlabeled learning for disease gene identification
title_short Ensemble positive unlabeled learning for disease gene identification
title_full Ensemble positive unlabeled learning for disease gene identification
title_fullStr Ensemble positive unlabeled learning for disease gene identification
title_full_unstemmed Ensemble positive unlabeled learning for disease gene identification
title_sort ensemble positive unlabeled learning for disease gene identification
publishDate 2014
url https://hdl.handle.net/10356/80041
http://hdl.handle.net/10220/19767
_version_ 1725985503901122560