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Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

ABSTRACT: BACKGROUND:Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. RESULTS:We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. CONCLUSIONS:The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.

SUBMITTER: Al-Aamri A

PROVIDER: S-EPMC6368766 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

Al-Aamri Amira A Taha Kamal K Al-Hammadi Yousof Y Maalouf Maher M Homouz Dirar D

BMC bioinformatics 20190208 1

<h4>Background</h4>Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-lin ...[more]

PMID: 30736752

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Project description:Although many cancer patients experience multiple concurrent symptoms, most studies have either focused on the analysis of single symptoms, or have used methods such as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations by first visually exploring the data to identify patterns in the co-occurrence of multiple symptoms, and then using those insights to select and develop quantitative measures to analyze and validate the results.We used networks to visualize how 665 cancer patients reported 18 symptoms, and then quantitatively analyzed the observed patterns using degree of symptom overlap between patients, degree of symptom clustering using network modularity, clustering of symptoms based on agglomerative hierarchical clustering, and degree of nestedness of the symptoms based on the most frequently co-occurring symptoms for different sizes of symptom sets. These results were validated by assessing the statistical significance of the quantitative measures through comparison with random networks of the same size and distribution.The cancer symptoms tended to co-occur in a nested structure, where there was a small set of symptoms that co-occurred in many patients, and progressively larger sets of symptoms that co-occurred among a few patients.These results suggest that cancer symptoms co-occur in a nested pattern as opposed to distinct clusters, thereby demonstrating the value of exploratory network analyses to reveal complex relationships between patients and symptoms. The research also extends methods for exploring symptom co-occurrence, including methods for quantifying the degree of symptom overlap and for examining nested co-occurrence in co-occurrence data. Finally, the analysis also suggested implications for the design of systems that assist in symptom assessment and management. The main limitation of the study was that only one dataset was considered, and future studies should attempt to replicate the results in new data.

Dataset Information

Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

Publications

Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

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