ISN: Inferring disease-related genes using seed gene and network analysis

Jeongwoo Kim, Sanghyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2012-2017
Number of pages6
ISBN (Electronic)9781538616451
DOIs
Publication statusPublished - 2017 Nov 27
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: 2017 Oct 52017 Oct 8

Publication series

Name2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
CountryCanada
CityBanff
Period17/10/517/10/8

Fingerprint

Network Analysis
Electric network analysis
Seed
Genes
Gene
Lung Cancer
Gene Networks
Prioritization
Text Mining
Biology

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

Cite this

Kim, J., & Park, S. (2017). ISN: Inferring disease-related genes using seed gene and network analysis. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (pp. 2012-2017). (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2017.8122914
Kim, Jeongwoo ; Park, Sanghyun. / ISN : Inferring disease-related genes using seed gene and network analysis. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2012-2017 (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017).
@inproceedings{633c8382bc4546daa1c74c37dae7dcb3,
title = "ISN: Inferring disease-related genes using seed gene and network analysis",
abstract = "In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.",
author = "Jeongwoo Kim and Sanghyun Park",
year = "2017",
month = "11",
day = "27",
doi = "10.1109/SMC.2017.8122914",
language = "English",
series = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2012--2017",
booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",
address = "United States",

}

Kim, J & Park, S 2017, ISN: Inferring disease-related genes using seed gene and network analysis. in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2012-2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, Canada, 17/10/5. https://doi.org/10.1109/SMC.2017.8122914

ISN : Inferring disease-related genes using seed gene and network analysis. / Kim, Jeongwoo; Park, Sanghyun.

2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2012-2017 (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - ISN

T2 - Inferring disease-related genes using seed gene and network analysis

AU - Kim, Jeongwoo

AU - Park, Sanghyun

PY - 2017/11/27

Y1 - 2017/11/27

N2 - In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.

AB - In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.

UR - http://www.scopus.com/inward/record.url?scp=85044197061&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044197061&partnerID=8YFLogxK

U2 - 10.1109/SMC.2017.8122914

DO - 10.1109/SMC.2017.8122914

M3 - Conference contribution

AN - SCOPUS:85044197061

T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017

SP - 2012

EP - 2017

BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Kim J, Park S. ISN: Inferring disease-related genes using seed gene and network analysis. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2012-2017. (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017). https://doi.org/10.1109/SMC.2017.8122914