Received: 21-12-2016
Accepted: 23-02-2017
DOI:
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Study Method Base on Biological Networks for Disease Candidate Gene Prediction
,
Tran Thi Thu Huyen
1
,
Nguyen Van Hoang
1
,
Nguyen Thi Huyen
1
,
Le Duc Hau
2
Keywords
Disease candidate gene prioritization, human signaling network, Boolean dynamics, network-based method, random walk with restart algorithm
Abstract
Predicting genes which may associate with disease is one of the important goals of biomedical research. There have been many computational methods developed to rank genes involved in a particular disease. However, due to the complex relationship between genes and the diseases, many genes that cause genetic diseases have not yet been discovered. The problem of ranking genes to identify the disease-associated gene has drawn attention of many researchers. To find a good method to predict target genes that cause diseases with high performance, we have conducted a survey of prediction methods based on biological network. We then proposed a new method using a Boolean network model. In biological network, defects by mutations on genes/proteins may cause a disease to occurin a person. Also, these mutations may affect other genes/proteins through structures of the biological networks. In this study, we proposed to use Boolean network model to assess the relevance of candidate genes to a disease of interest by measuring the degree of mutational effect from known disease-associated genes to candidate genes. Particularly, we mutated known disease-associated genes and measured the effect of this mutation on candidate genes based on Boolean dynamics of biological networks. Based on this measured value, candidate genes can be prioritized and finally top-ranked candidate genes can be selected as novel promising disease genes. Simulation results on a set of diseases showed that the proposed method is superior to a state-of-the-art one, which is based on a random walk with a restart algorithm. Using the proposed method, we have identified 27 genes associated with breast cancer with evidences from literature.
References
Adie E., R. A. (2005). Speeding disease gene discovery by sequence based candidate. BMC Bioinformatics, 6: 55.
Aerts S., D. L. (2006). Gene prioritization through genomic data fusion. Nature Biotechnology, 24: 537-544.
Albert, R. (2004). Boolean Modeling of Genetic Regulatory Networks. Lecture Notes in Physics.,650: 459-481.
Albert, R. (2004). Boolean Modeling of Genetic Regulatory Networks. Lect. Notes Phys., 650: 459-481.
Amberger J., C. A. (2009). McKusick's Online Mendelian Inheritance in Man (OMIM®). Nucleic Acids Research, 37: D793-D796.
Calvo S., M. J. (2006). Systematic identification of human. Nat Genet, 38: 576-582.
Cui Q., E. P. (2009). Protein evolution on a human. BMC Systems Biology, 3: 21.
Đặng Vũ Tùng, D. A. (2015). Phân hạng gen gây bệnh sử dụng học tăng kết hợp với xác suất tiền nghiệm. Các công trình nghiên cứu, phát triển và ứng dụng CNTT-TT, Tập V-1, Số13 (33).
Duc-Hau Le, Y.-K. K. (2012). A Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection. Computational Biology and Chemistry, pp. 17-23.
Faure A., A. N. (2006). Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics, 22: e124-131.
Hanley JA, M. B. (1982). The meaning and use of the area under the Receiver Operating Characteristic (ROC) curve. Radiology, 143: 29-36.
Kann, M. G. (2010). Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefings in Bioinformatics, 11: 96-110.
Kauffman S., C. P. (2003). Random Boolean network models and the yeast transcriptional. Proceedings of the National Academy of Sciences, 100: 14796-14799.
Keerthikumar S., S. B. (2009). Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach. DNA Research, 16: 345-351.
Kwon, D.-H. L.-K. (2011). The effects of feedback loops on disease comorbidity in human signaling networks. Bioinformatics, 27: 1113-1120.
Kwon, D.-H. L.-K. (2013). A coherent feedforward loop design principle to sustain robustness of biological networks. Bioinformatics, 29: 630-637.
Kwon, D.-H. Le and Y.-K. (2011). NetDS: a Cytoscape plugin to analyze the robustness of dynamics and feedforward/feedback loop structures of biological networks. Bioinformatics, 27: 2767-2768.
Li, J. X. (2006). Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics, 22: 2800-2805.
LOVASZ, L. (1996). Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty, 2: 353-398.
Sun J., J. C. (2009). Functional link artificial neural. In Neural Networks.
Trần Thị Bích Phương, N. V. (2013). Một phương pháp phân tích mạng tương tác protein để dự đoán gen gây bệnh ung thư. journalof science of hnue, 58: 38-46.
Trinh H.-C., D.-H. L.-K. (2014). PANET: A GPU-Based Tool for Fast Parallel Analysis of Robustness Dynamics and Feed-Forward/Feedback Loop Structures in Large-Scale Biologica lNetworks. PLoS ONE, 9: e103010.
Vali derhami, E. K. (2013). Applying reinforcement learning for web pages ranking algorithms. Applied Soft Computing, 13: 1686-1692.
Vanunu O, M. O. (2010). Associating genes and protein complexes with disease via network propagation. PLoSComput Biol, 6: e1000641.
Wang X., N. G. (2011). Network-based methods for human disease gene prediction. Briefings in Functional, 10: 280-293.