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

Jeongwoo Kim; Sanghyun Park

doi:10.1109/SMC.2017.8122914

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

Jeongwoo Kim, Sanghyun Park

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2012-2017
Number of pages	6
ISBN (Electronic)	9781538616451
DOIs	https://doi.org/10.1109/SMC.2017.8122914
Publication status	Published - 2017 Nov 27
Event	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada Duration: 2017 Oct 5 → 2017 Oct 8

Publication series

Name	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume	2017-January

Other

Other	2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Country/Territory	Canada
City	Banff
Period	17/10/5 → 17/10/8

Bibliographical note

Funding Information:
Sanghyun Park is the corresponding author. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2017M2A2A7A02020213).

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SMC.2017.8122914

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

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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",

note = "Funding Information: Sanghyun Park is the corresponding author. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2017M2A2A7A02020213). Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 ; Conference date: 05-10-2017 Through 08-10-2017",

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doi = "10.1109/SMC.2017.8122914",

language = "English",

series = "2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017",

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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 proceeding › Conference contribution

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T1 - ISN

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AU - Kim, Jeongwoo

AU - Park, Sanghyun

N1 - Funding Information: Sanghyun Park is the corresponding author. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2017M2A2A7A02020213). Publisher Copyright: © 2017 IEEE.

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.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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