IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v496y2018icp53-61.html
   My bibliography  Save this article

Discovering disease-associated genes in weighted protein–protein interaction networks

Author

Listed:
  • Cui, Ying
  • Cai, Meng
  • Stanley, H. Eugene

Abstract

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight – which quantifies their relative strength – into consideration. We use connection weights in a protein–protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype–phenotype associations.

Suggested Citation

  • Cui, Ying & Cai, Meng & Stanley, H. Eugene, 2018. "Discovering disease-associated genes in weighted protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 53-61.
  • Handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:53-61
    DOI: 10.1016/j.physa.2017.12.080
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117313298
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2017.12.080?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wu, Shun-yao & Shao, Feng-jing & Sun, Ren-cheng & Sui, Yi & Wang, Ying & Wang, Jin-long, 2014. "Analysis of human genes with protein–protein interaction network for detecting disease genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 217-228.
    2. U Martin Singh-Blom & Nagarajan Natarajan & Ambuj Tewari & John O Woods & Inderjit S Dhillon & Edward M Marcotte, 2013. "Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-17, May.
    3. Jianhua Li & Xiaoyan Lin & Yueyang Teng & Shouliang Qi & Dayu Xiao & Jianying Zhang & Yan Kang, 2016. "A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    4. Oron Vanunu & Oded Magger & Eytan Ruppin & Tomer Shlomi & Roded Sharan, 2010. "Associating Genes and Protein Complexes with Disease via Network Propagation," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-9, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. T M Murali & Matthew D Dyer & David Badger & Brett M Tyler & Michael G Katze, 2011. "Network-Based Prediction and Analysis of HIV Dependency Factors," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-15, September.
    2. Li-Chen Hung & Pei-Tseng Kung & Chi-Hsuan Lung & Ming-Hsui Tsai & Shih-An Liu & Li-Ting Chiu & Kuang-Hua Huang & Wen-Chen Tsai, 2020. "Assessment of the Risk of Oral Cancer Incidence in A High-Risk Population and Establishment of A Predictive Model for Oral Cancer Incidence Using A Population-Based Cohort in Taiwan," IJERPH, MDPI, vol. 17(2), pages 1-15, January.
    3. Jianhua Li & Xiaoyan Lin & Yueyang Teng & Shouliang Qi & Dayu Xiao & Jianying Zhang & Yan Kang, 2016. "A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    4. Le Ou-Yang & Dao-Qing Dai & Xiao-Fei Zhang, 2013. "Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-18, May.
    5. Akram Vasighizaker & Alok Sharma & Abdollah Dehzangi, 2019. "A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-12, December.
    6. Mengyun Yang & Huimin Luo & Yaohang Li & Fang-Xiang Wu & Jianxin Wang, 2019. "Overlap matrix completion for predicting drug-associated indications," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.
    7. U Martin Singh-Blom & Nagarajan Natarajan & Ambuj Tewari & John O Woods & Inderjit S Dhillon & Edward M Marcotte, 2013. "Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-17, May.
    8. MaoQiang Xie & YingJie Xu & YaoGong Zhang & TaeHyun Hwang & Rui Kuang, 2015. "Network-based Phenome-Genome Association Prediction by Bi-Random Walk," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    9. Konstantina Charmpi & Manopriya Chokkalingam & Ronja Johnen & Andreas Beyer, 2021. "Optimizing network propagation for multi-omics data integration," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-26, November.
    10. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    11. Deborah Chasman & Brandi Gancarz & Linhui Hao & Michael Ferris & Paul Ahlquist & Mark Craven, 2014. "Inferring Host Gene Subnetworks Involved in Viral Replication," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-22, May.
    12. Xing Chen & Jun Yin & Jia Qu & Li Huang, 2018. "MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-24, August.
    13. Elisa Salviato & Vera Djordjilović & Monica Chiogna & Chiara Romualdi, 2019. "SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-28, October.
    14. Abby Hill & Scott Gleim & Florian Kiefer & Frederic Sigoillot & Joseph Loureiro & Jeremy Jenkins & Melody K Morris, 2019. "Benchmarking network algorithms for contextualizing genes of interest," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-14, December.
    15. Wen Zhang & Xiang Yue & Guifeng Tang & Wenjian Wu & Feng Huang & Xining Zhang, 2018. "SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-21, December.
    16. Florin Ratajczak & Mitchell Joblin & Marcel Hildebrandt & Martin Ringsquandl & Pascal Falter-Braun & Matthias Heinig, 2023. "Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    17. Daniel E Carlin & Barry Demchak & Dexter Pratt & Eric Sage & Trey Ideker, 2017. "Network propagation in the cytoscape cyberinfrastructure," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-9, October.
    18. Juan J Cáceres & Alberto Paccanaro, 2019. "Disease gene prediction for molecularly uncharacterized diseases," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-14, July.
    19. Joana P Gonçalves & Alexandre P Francisco & Yves Moreau & Sara C Madeira, 2012. "Interactogeneous: Disease Gene Prioritization Using Heterogeneous Networks and Full Topology Scores," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-13, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:53-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.