Ranked prediction of p53 targets using hidden variable dynamic modeling

被引：90

作者：

Barenco, M

Tomescu, D

Brewer, D

Callard, R

Stark, J

Hubank, M

机构：

[1] UCL, Inst Child Hlth, London WC1N 1EH, England

[2] UCL, CoMPLEX, London NW1 2HE, England

[3] Univ London Imperial Coll Sci Technol & Med, Dept Math, London SW7 2AZ, England

来源：

GENOME BIOLOGY | 2006年 / 7卷 / 03期

基金：

英国生物技术与生命科学研究理事会;

关键词：

D O I：

10.1186/gb-2006-7-3-r25

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.

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页数：18

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