The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
마이크로어레이 기술은 다양한 생물학 및 의학 연구 분야에 적용되어 왔습니다. 마이크로어레이 데이터 세트에서 정보를 추출하기 위한 예비 단계는 마이크로어레이 데이터 간에 차별적으로 발현된 유전자를 식별하는 것입니다. 차등적으로 발현되는 유전자와 일반적으로 연관된 GO 용어를 식별하면 자극 의존적 또는 질병 관련 유전자 및 생물학적 사건 등을 찾을 수 있습니다. 그러나 유전자 세트 농축 분석(GSEA)을 포함한 일반적인 접근법을 통해 규제가 완화된 GO 용어를 식별합니다. )는 마이크로어레이 데이터 세트 중 특정 데이터에서 과도하게 표현된 GO 용어(즉, 데이터별 GO 용어)를 반드시 제공하지는 않습니다. 본 논문에서는 데이터별 GO 항을 정확하게 식별하고 실제 마이크로어레이 데이터 세트를 이용한 시뮬레이션을 통해 가용성을 추정하는 통계적 방법을 제안합니다.
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부
Yoichi YAMADA, Ken-ichi HIROTANI, Kenji SATOU, Ken-ichiro MURAMOTO, "An Identification Method of Data-Specific GO Terms from a Microarray Data Set" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1093-1102, May 2009, doi: 10.1587/transinf.E92.D.1093.
Abstract: Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1093/_p
부
@ARTICLE{e92-d_5_1093,
author={Yoichi YAMADA, Ken-ichi HIROTANI, Kenji SATOU, Ken-ichiro MURAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={An Identification Method of Data-Specific GO Terms from a Microarray Data Set},
year={2009},
volume={E92-D},
number={5},
pages={1093-1102},
abstract={Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.},
keywords={},
doi={10.1587/transinf.E92.D.1093},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - An Identification Method of Data-Specific GO Terms from a Microarray Data Set
T2 - IEICE TRANSACTIONS on Information
SP - 1093
EP - 1102
AU - Yoichi YAMADA
AU - Ken-ichi HIROTANI
AU - Kenji SATOU
AU - Ken-ichiro MURAMOTO
PY - 2009
DO - 10.1587/transinf.E92.D.1093
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.
ER -