Analysis of metabolic and regulatory pathways through Gene Ontology-derived semantic similarity measures.

Xiang Guo; Craig D. Shriver; Hai Hu; Michael N. Liebman

Analysis of metabolic and regulatory pathways through Gene Ontology-derived semantic similarity measures.

Xiang Guo^*, Craig D. Shriver, Hai Hu, Michael N. Liebman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.

Original language	English
Pages (from-to)	972
Number of pages	1
Journal	AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
State	Published - 2005
Externally published	Yes

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title = "Analysis of metabolic and regulatory pathways through Gene Ontology-derived semantic similarity measures.",

abstract = "This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.",

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journal = "AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium",

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AU - Guo, Xiang

AU - Shriver, Craig D.

AU - Hu, Hai

AU - Liebman, Michael N.

PY - 2005

Y1 - 2005

N2 - This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.

AB - This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.

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M3 - Article

C2 - 16779259

AN - SCOPUS:39049178598

SN - 1559-4076

SP - 972

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

ER -

Analysis of metabolic and regulatory pathways through Gene Ontology-derived semantic similarity measures.

Abstract

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