Abstract
BackgroundPredicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN).ResultsConsidering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia.ConclusionsTissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.
Highlights
Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology
The same method implemented on the weighed the constructed tissue-specific network (WTSN) can achieve better results compared to those being implemented on original protein interaction networks (PIN), weighted PIN (WPIN), or tissue-specific networks (TSN)
We applied the PageRank-based method to these four different types of protein interaction networks, and the corresponding results were marked as PR, SPR, WPR, SWPR, respectively
Summary
Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. Network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Many network-based methods have been proposed to predict protein functions, identify essential proteins and disease-related genes and complexes [5,6,7]. Great progresses have been made on the network-based methods, it is still a challenge task to identify disease-related genes as the considerable false-positives are still existed in the current available PINs [25]. DNA methylation information can be used to improve identification of disease-related genes. For prioritizing cancer-related genes, Liu et al [32] constructed a weighted human protein interaction network by using DNA methylation correlations
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