SSL: Inferring disease-related genes using sentence structure and literature data

Jeongwoo Kim; Won Gi Choi; Jungrim Kim; Sanghyun Park

doi:10.1109/BIGCOMP.2017.7881723

SSL: Inferring disease-related genes using sentence structure and literature data

Jeongwoo Kim, Won Gi Choi, Jungrim Kim, Sanghyun Park

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Text mining is widely applied in biology to infer relationships between biological entities. In biology, disease-gene relationships are important to discover the cause of disease. Therefore, we propose a useful method called SSL, which infers disease-related genes, using sentence structure and literature data. Using sentence structure, the proposed method decreases the number of candidate disease-related genes and infers more meaningful disease-related genes than other comparable methods. Furthermore, our method extracts useful sentences that have information on the relationship between specific diseases and genes. By analyzing the structure of the sentences, we can obtain useful knowledge of disease-gene relationships. We applied our method to five diseases, including Alzheimer's disease, prostate cancer, gastric cancer, colorectal cancer, and lung cancer. For validation, we investigated the top 10 inferred genes for five diseases. Our method demonstrated up to 50% higher precision than existing methods, and showed 98% accuracy in inferring disease-related genes.

Original language	English
Title of host publication	2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	100-107
Number of pages	8
ISBN (Electronic)	9781509030156
DOIs	https://doi.org/10.1109/BIGCOMP.2017.7881723
Publication status	Published - 2017 Mar 17
Event	2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of Duration: 2017 Feb 13 → 2017 Feb 16

Publication series

Name	2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017

Other

Other	2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
Country	Korea, Republic of
City	Jeju Island
Period	17/2/13 → 17/2/16

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Information Systems
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.1109/BIGCOMP.2017.7881723

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Kim, J., Choi, W. G., Kim, J., & Park, S. (2017). SSL: Inferring disease-related genes using sentence structure and literature data. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 (pp. 100-107). [7881723] (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2017.7881723

Kim, Jeongwoo ; Choi, Won Gi ; Kim, Jungrim ; Park, Sanghyun. / SSL : Inferring disease-related genes using sentence structure and literature data. 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 100-107 (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017).

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abstract = "Text mining is widely applied in biology to infer relationships between biological entities. In biology, disease-gene relationships are important to discover the cause of disease. Therefore, we propose a useful method called SSL, which infers disease-related genes, using sentence structure and literature data. Using sentence structure, the proposed method decreases the number of candidate disease-related genes and infers more meaningful disease-related genes than other comparable methods. Furthermore, our method extracts useful sentences that have information on the relationship between specific diseases and genes. By analyzing the structure of the sentences, we can obtain useful knowledge of disease-gene relationships. We applied our method to five diseases, including Alzheimer's disease, prostate cancer, gastric cancer, colorectal cancer, and lung cancer. For validation, we investigated the top 10 inferred genes for five diseases. Our method demonstrated up to 50% higher precision than existing methods, and showed 98% accuracy in inferring disease-related genes.",

author = "Jeongwoo Kim and Choi, {Won Gi} and Jungrim Kim and Sanghyun Park",

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year = "2017",

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doi = "10.1109/BIGCOMP.2017.7881723",

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Kim, J, Choi, WG, Kim, J & Park, S 2017, SSL: Inferring disease-related genes using sentence structure and literature data. in 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017., 7881723, 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, Institute of Electrical and Electronics Engineers Inc., pp. 100-107, 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, Jeju Island, Korea, Republic of, 17/2/13. https://doi.org/10.1109/BIGCOMP.2017.7881723

SSL : Inferring disease-related genes using sentence structure and literature data. / Kim, Jeongwoo; Choi, Won Gi; Kim, Jungrim; Park, Sanghyun.

2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 100-107 7881723 (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kim, Jungrim

AU - Park, Sanghyun

PY - 2017/3/17

Y1 - 2017/3/17

N2 - Text mining is widely applied in biology to infer relationships between biological entities. In biology, disease-gene relationships are important to discover the cause of disease. Therefore, we propose a useful method called SSL, which infers disease-related genes, using sentence structure and literature data. Using sentence structure, the proposed method decreases the number of candidate disease-related genes and infers more meaningful disease-related genes than other comparable methods. Furthermore, our method extracts useful sentences that have information on the relationship between specific diseases and genes. By analyzing the structure of the sentences, we can obtain useful knowledge of disease-gene relationships. We applied our method to five diseases, including Alzheimer's disease, prostate cancer, gastric cancer, colorectal cancer, and lung cancer. For validation, we investigated the top 10 inferred genes for five diseases. Our method demonstrated up to 50% higher precision than existing methods, and showed 98% accuracy in inferring disease-related genes.

AB - Text mining is widely applied in biology to infer relationships between biological entities. In biology, disease-gene relationships are important to discover the cause of disease. Therefore, we propose a useful method called SSL, which infers disease-related genes, using sentence structure and literature data. Using sentence structure, the proposed method decreases the number of candidate disease-related genes and infers more meaningful disease-related genes than other comparable methods. Furthermore, our method extracts useful sentences that have information on the relationship between specific diseases and genes. By analyzing the structure of the sentences, we can obtain useful knowledge of disease-gene relationships. We applied our method to five diseases, including Alzheimer's disease, prostate cancer, gastric cancer, colorectal cancer, and lung cancer. For validation, we investigated the top 10 inferred genes for five diseases. Our method demonstrated up to 50% higher precision than existing methods, and showed 98% accuracy in inferring disease-related genes.

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Kim J, Choi WG, Kim J, Park S. SSL: Inferring disease-related genes using sentence structure and literature data. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 100-107. 7881723. (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017). https://doi.org/10.1109/BIGCOMP.2017.7881723

SSL: Inferring disease-related genes using sentence structure and literature data

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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