PRINCIPLE: a tool for associating genes with diseases via network propagation

Gottlieb, Assaf; Magger, Oded; Berman, Igor; Ruppin, Eytan; Sharan, Roded

doi:10.1093/bioinformatics/btr584

Cited by 46 publications

(27 citation statements)

References 19 publications

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“…In order to evaluate the power of different interactome datasets, we used network centrality to rank genes in this study. Although other well-established disease gene prioritization methods like DAPPLE [29], Metaranker [30], PRINCE [31] and some random walk methods [32, 33] have been demonstrated to be valuable methods for predicting disease related genes, it is complex to compare these different methods in multiple network contexts.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Sahni

2016

Oncotarget

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Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

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Section: Discussionmentioning

confidence: 99%

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Sahni

2016

Oncotarget

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“…Fig. 1 shows the gene network for prostate cancer constructed by the PRINCIPLE [25] tool. The PRINCIPLE tool describes gene networks based on the PRINCE algorithm.…”

Section: Network In Biologymentioning

confidence: 99%

“…Furthermore, the experimental results are influenced by the choice of the seed gene. The PRINCE algorithm [24,25] is another method that was developed to infer relationships among genes and diseases using network analysis based on diseasedisease similarity and protein-protein interaction data. The PRINCE algorithm can be applied to all diseases; however, it is less accurate than the method by Ozgur et al…”

Section: Introductionmentioning

confidence: 99%

LGscore: A method to identify disease-related genes using biological literature and Google data

Kim

Yong-Jin

et al. 2015

Journal of Biomedical Informatics

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Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene-gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease-gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer's disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods.

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“…Cytoscape plugins such as APID2NET (Hernandez-Toro et al, 2007) and PRINCIPLE (Gottlieb et al, 2011) are also very pertinent. APID2NET retrieves PPI data from the APID server for further analysis within the Cytoscape environment.…”

Section: Visualization Toolsmentioning

confidence: 99%