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.
Highlights
While high-throughput genomic studies have led to the discovery of hundreds and thousands of candidate disease genes, the identification of genes involved in specific human diseases has remained a fundamental challenge, requiring time-consuming and expensive experimentation
Given that an increasing number of genes have been experimentally confirmed over the years as causative genes to various human diseases, it will be useful to develop machine learning methods to identify novel disease genes from the confirmed disease genes as positive training examples, based on the observation that genes associated with similar disease phenotypes are likely to share similar biological characteristics
We compare the performance of EPU with its base learning models, namely Multi-level Support Vector Machine (MSVM), Weighted K-Nearest Neighbor (WKNN) and Weighted Naıve Bayes (WNB)
Summary
While high-throughput genomic studies have led to the discovery of hundreds and thousands of candidate disease genes, the identification of genes involved in specific human diseases has remained a fundamental challenge, requiring time-consuming and expensive experimentation. Given that an increasing number of genes have been experimentally confirmed over the years as causative genes to various human diseases, it will be useful to develop machine learning methods to identify novel disease genes from the confirmed disease genes as positive training examples, based on the observation that genes associated with similar disease phenotypes are likely to share similar biological characteristics. Proteins involved in hereditary diseases tend to be long, with more homologs with distant species, but fewer paralogs within human genome [1]. They are likely to attach together to form functional modules such as protein complexes [2]. Various studies have shown that genes associated with similar disorders tend to demonstrate similar gene expression profiling [3], high functional similarities [4] [5] and physical interactions between their gene products [6] [7]
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