Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Takeyuki Tamura; Tatsuya Akutsu; Chun-Yu Lin; Jinn-Moon Yang

doi:10.1109/BIBE.2016.25

Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Takeyuki Tamura, Tatsuya Akutsu, Chun-Yu Lin, Jinn-Moon Yang

Institute of Bioinformatics and Systems Biology

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

2 Scopus citations

Abstract

Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	57-63
Number of pages	7
ISBN (Electronic)	9781509038336
DOIs	https://doi.org/10.1109/BIBE.2016.25
State	Published - 16 Dec 2016
Event	16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016 - Taichung, Taiwan Duration: 31 Oct 2016 → 2 Nov 2016

Publication series

Name	Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

Conference

Conference	16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016
Country/Territory	Taiwan
City	Taichung
Period	31/10/16 → 2/11/16

Keywords

Driver genes
Gene expression
Marker genes
Metabolic networks

UN SDGs

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

Access to Document

10.1109/BIBE.2016.25

Cite this

Tamura, T., Akutsu, T., Lin, C.-Y., & Yang, J.-M. (2016). Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. In Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016 (pp. 57-63). Article 7789960 (Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE.2016.25

Tamura, Takeyuki ; Akutsu, Tatsuya ; Lin, Chun-Yu et al. / Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 57-63 (Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016).

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title = "Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks",

abstract = "Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.",

keywords = "Driver genes, Gene expression, Marker genes, Metabolic networks",

author = "Takeyuki Tamura and Tatsuya Akutsu and Chun-Yu Lin and Jinn-Moon Yang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016 ; Conference date: 31-10-2016 Through 02-11-2016",

year = "2016",

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doi = "10.1109/BIBE.2016.25",

language = "English",

series = "Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016",

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Tamura, T, Akutsu, T, Lin, C-Y & Yang, J-M 2016, Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. in Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016., 7789960, Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 57-63, 16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016, Taichung, Taiwan, 31/10/16. https://doi.org/10.1109/BIBE.2016.25

Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. / Tamura, Takeyuki; Akutsu, Tatsuya; Lin, Chun-Yu et al.
Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 57-63 7789960 (Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016).

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

TY - GEN

T1 - Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

AU - Tamura, Takeyuki

AU - Akutsu, Tatsuya

AU - Lin, Chun-Yu

AU - Yang, Jinn-Moon

PY - 2016/12/16

Y1 - 2016/12/16

N2 - Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

AB - Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

KW - Driver genes

KW - Gene expression

KW - Marker genes

KW - Metabolic networks

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U2 - 10.1109/BIBE.2016.25

DO - 10.1109/BIBE.2016.25

M3 - Conference contribution

AN - SCOPUS:85011026075

T3 - Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

SP - 57

EP - 63

BT - Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016

Y2 - 31 October 2016 through 2 November 2016

ER -

Tamura T, Akutsu T, Lin CY , Yang JM. Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. In Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 57-63. 7789960. (Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016). doi: 10.1109/BIBE.2016.25

Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Abstract

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Keywords

UN SDGs

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