The Research on Gene-disease Association Based on Text-mining of PubMed

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2018 Feb 9

PMID 29415654

Citations 21

Authors

Jie Zhou

Bo-Quan Fu

Affiliations

Soon will be listed here.

Abstract

Background: The associations between genes and diseases are of critical significance in aspects of prevention, diagnosis and treatment. Although gene-disease relationships have been investigated extensively, much of the underpinnings of these associations are yet to be elucidated.

Methods: A novel method integrates MeSH database, term weight (TW), and co-occurrence methods to predict gene-disease associations based on the cosine similarity between gene vectors and disease vectors. Vectors are transformed from the texts of documents in the PubMed database according to the appearance and location of the gene or disease terms. The disease related text data has been optimized during the process of constructing vectors.

Results: The overall distribution of cosine similarity value was investigated. By using the gene-disease association data in OMIM database as golden standard, the performance of cosine similarity in predicting gene-disease linkage was evaluated. The effects of applying weight matrix, penalty weights for keywords (PWK), and normalization were also investigated. Finally, we demonstrated that our method outperforms heterogeneous network edge prediction (HNEP) in aspects of precision rate and recall rate.

Conclusions: Our method proposed in this paper is easy to be conducted and the results can be integrated with other models to improve the overall performance of gene-disease association predictions.

Citing Articles

Literature mining discerns latent disease-gene relationships.

Rai P, Jain A, Kumar S, Sharma D, Jha N, Chawla S Bioinformatics. 2024; 40(4).

PMID: 38608194 PMC: 11060865. DOI: 10.1093/bioinformatics/btae185.

Robustness evaluations of pathway activity inference methods on gene expression data.

Hui T, Kasim S, Abdul Aziz I, Fudzee M, Haron N, Sutikno T BMC Bioinformatics. 2024; 25(1):23.

PMID: 38216898 PMC: 10785356. DOI: 10.1186/s12859-024-05632-w.

MantaID: a machine learning-based tool to automate the identification of biological database IDs.

Zeng Z, Hu J, Cao M, Li B, Wang X, Yu F Database (Oxford). 2023; 2023.

PMID: 37159241 PMC: 10168000. DOI: 10.1093/database/baad028.

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.

Supianto A, Nurdiansyah R, Weng C, Zilvan V, Yuwana R, Arisal A J Taibah Univ Med Sci. 2023; 18(4):787-801.

PMID: 36618881 PMC: 9810500. DOI: 10.1016/j.jtumed.2022.12.015.

Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.

Nicholson D, Himmelstein D, Greene C BioData Min. 2022; 15(1):26.

PMID: 36258252 PMC: 9578183. DOI: 10.1186/s13040-022-00311-z.

References

Trindade D, Orsine L, Barbosa-Silva A, Donnard E, Ortega J . A guide for building biological pathways along with two case studies: hair and breast development. Methods. 2014; 74:16-35. DOI: 10.1016/j.ymeth.2014.10.006. View

Tsai R, Lai P . Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles. BMC Bioinformatics. 2011; 12:60. PMC: 3053256. DOI: 10.1186/1471-2105-12-60. View

Natarajan N, Dhillon I . Inductive matrix completion for predicting gene-disease associations. Bioinformatics. 2014; 30(12):i60-68. PMC: 4058925. DOI: 10.1093/bioinformatics/btu269. View

Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R . Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol. 2010; 6(1):e1000641. PMC: 2797085. DOI: 10.1371/journal.pcbi.1000641. View

Singh-Blom U, Natarajan N, Tewari A, Woods J, Dhillon I, Marcotte E . Prediction and validation of gene-disease associations using methods inspired by social network analyses. PLoS One. 2013; 8(5):e58977. PMC: 3641094. DOI: 10.1371/journal.pone.0058977. View

Gonzalez G, Tahsin T, Goodale B, Greene A, Greene C . Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery. Brief Bioinform. 2015; 17(1):33-42. PMC: 4719073. DOI: 10.1093/bib/bbv087. View

Kohler S, Bauer S, Horn D, Robinson P . Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet. 2008; 82(4):949-58. PMC: 2427257. DOI: 10.1016/j.ajhg.2008.02.013. View

Fleuren W, Alkema W . Application of text mining in the biomedical domain. Methods. 2015; 74:97-106. DOI: 10.1016/j.ymeth.2015.01.015. View

Fontaine J, Priller J, Spruth E, Perez-Iratxeta C, Andrade-Navarro M . Assessment of curated phenotype mining in neuropsychiatric disorder literature. Methods. 2014; 74:90-6. DOI: 10.1016/j.ymeth.2014.11.022. View

10.

Pletscher-Frankild S, Palleja A, Tsafou K, Binder J, Jensen L . DISEASES: text mining and data integration of disease-gene associations. Methods. 2014; 74:83-9. DOI: 10.1016/j.ymeth.2014.11.020. View

11.

Davis A, Wiegers T, Johnson R, Lay J, Lennon-Hopkins K, Saraceni-Richards C . Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database. PLoS One. 2013; 8(4):e58201. PMC: 3629079. DOI: 10.1371/journal.pone.0058201. View

12.

Wu Y, Liu M, Zheng W, Zhao Z, Xu H . Ranking gene-drug relationships in biomedical literature using Latent Dirichlet Allocation. Pac Symp Biocomput. 2011; :422-33. PMC: 4095990. View

13.

Frijters R, van Vugt M, Smeets R, van Schaik R, de Vlieg J, Alkema W . Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Comput Biol. 2010; 6(9). PMC: 2944780. DOI: 10.1371/journal.pcbi.1000943. View

14.

Oti M, Snel B, Huynen M, Brunner H . Predicting disease genes using protein-protein interactions. J Med Genet. 2006; 43(8):691-8. PMC: 2564594. DOI: 10.1136/jmg.2006.041376. View

15.

Kim J, Kim H, Yoon Y, Park S . LGscore: A method to identify disease-related genes using biological literature and Google data. J Biomed Inform. 2015; 54:270-82. DOI: 10.1016/j.jbi.2015.01.003. View

16.

van Driel M, Bruggeman J, Vriend G, Brunner H, Leunissen J . A text-mining analysis of the human phenome. Eur J Hum Genet. 2006; 14(5):535-42. DOI: 10.1038/sj.ejhg.5201585. View

17.

Van Landeghem S, de Bodt S, Drebert Z, Inze D, Van de Peer Y . The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis. Plant Cell. 2013; 25(3):794-807. PMC: 3634689. DOI: 10.1105/tpc.112.108753. View

18.

Papanikolaou N, Pavlopoulos G, Theodosiou T, Iliopoulos I . Protein-protein interaction predictions using text mining methods. Methods. 2014; 74:47-53. DOI: 10.1016/j.ymeth.2014.10.026. View

19.

Wang X, Gulbahce N, Yu H . Network-based methods for human disease gene prediction. Brief Funct Genomics. 2011; 10(5):280-93. DOI: 10.1093/bfgp/elr024. View

20.

Radivojac P, Peng K, Clark W, Peters B, Mohan A, Boyle S . An integrated approach to inferring gene-disease associations in humans. Proteins. 2008; 72(3):1030-7. PMC: 2824611. DOI: 10.1002/prot.21989. View