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. 2019 Feb 8;20(1):70.
doi: 10.1186/s12859-019-2634-7.

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

Amira Al-Aamri  1 , Kamal Taha  1 , Yousof Al-Hammadi  1 , Maher Maalouf  2 , Dirar Homouz  3
Affiliations

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

Amira Al-Aamri et al. BMC Bioinformatics. .

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.

Keywords: Biological NLP; Biomedical literature; Disease-gene association; Genetic network; Text mining.

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Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Number of new cases and deaths for each common cancer type from NIH [2]
Fig. 2
Fig. 2
ROC curve for Training the data using WLR. TPR is increased at low FPR
Fig. 3
Fig. 3
Precision-Recall Curve Using WLR
Fig. 4
Fig. 4
Precision-Recall Curve Using WKLR
Fig. 5
Fig. 5
The process of network analysis and disease-gene identification
Fig. 6
Fig. 6
The prediction is made over several thresholds. As the threshold increases, fewer pairs are assigned to the positive class
See this image and copyright information in PMC

References

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