Literature DB >> 29763853

Text mining and network analysis to find functional associations of genes in high altitude diseases.

Balu Bhasuran1, Devika Subramanian2, Jeyakumar Natarajan3.   

Abstract

BACKGROUND AND OBJECTIVES: Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases.
METHOD: In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships.
RESULTS: Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated.
CONCLUSION: The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Gene co-occurrence; High altitude diseases; Network analysis; Text mining

Mesh:

Year:  2018        PMID: 29763853     DOI: 10.1016/j.compbiolchem.2018.05.002

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  3 in total

1.  Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.

Authors:  Balu Bhasuran
Journal:  Methods Mol Biol       Date:  2022

2.  BioBERT and Similar Approaches for Relation Extraction.

Authors:  Balu Bhasuran
Journal:  Methods Mol Biol       Date:  2022

3.  Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer's Disease: Insights From Data Mining and Enrichment Analysis.

Authors:  Cristina Battaglia; Marco Venturin; Aleksandra Sojic; Nithiya Jesuthasan; Alessandro Orro; Roberta Spinelli; Massimo Musicco; Gianluca De Bellis; Fulvio Adorni
Journal:  Front Genet       Date:  2019-09-24       Impact factor: 4.599

  3 in total

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