Literature DB >> 29155333

IMA: Identifying disease-related genes using MeSH terms and association rules.

Jeongwoo Kim1, Changbae Bang2, Hyeonseo Hwang3, Doyoung Kim4, Chihyun Park5, Sanghyun Park6.   

Abstract

Genes play an important role in several diseases. Hence, in biology, identifying relationships between diseases and genes is important for the analysis of diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using MeSH terms and association rules. We identified genes by analyzing the MeSH terms and extracted information on gene-gene interactions based on association rules. By integrating the extracted interactions, we constructed gene-gene networks and identified disease-related genes. We applied the proposed method to study five cancers, including prostate, lung, breast, stomach, and colorectal cancer, and demonstrated that the proposed method is more useful for identifying disease-related and candidate disease-related genes than previously published methods. In this study, we identified 20 genes for each disease. Among them, we presented 34 important candidate genes with evidence that supports the relationship of the candidate genes with diseases.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Association rules; Data mining; Disease; Gene

Mesh:

Year:  2017        PMID: 29155333     DOI: 10.1016/j.jbi.2017.11.009

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  3 in total

1.  Finding Gene Associations by Text Mining and Annotating it with Gene Ontology.

Authors:  Oviya Ramalakshmi Iyyappan; Sharanya Manoharan
Journal:  Methods Mol Biol       Date:  2022

2.  Brand Marketing Leveraging the Advantage of Emoji Pack Relying on Association Rule Algorithm in Data Mining Technology.

Authors:  Huan Tian
Journal:  Comput Intell Neurosci       Date:  2022-05-24

3.  Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis.

Authors:  Mireya Martínez-García; José Manuel Villegas Camacho; Enrique Hernández-Lemus
Journal:  Front Public Health       Date:  2022-03-29
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.