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RECOMB
2004
Springer
14 years 5 months ago
Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple orga...
Manuel Middendorf, Anshul Kundaje, Chris Wiggins, ...
BMCBI
2006
216views more  BMCBI 2006»
13 years 5 months ago
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data
Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfu...
Haiying Wang, Huiru Zheng, David Simpson, Francisc...
BMCBI
2006
110views more  BMCBI 2006»
13 years 5 months ago
Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae
Background: Reverse-engineering regulatory networks is one of the central challenges for computational biology. Many techniques have been developed to accomplish this by utilizing...
Shawn Cokus, Sherri Rose, David Haynor, Niels Gr&o...
BMCBI
2006
122views more  BMCBI 2006»
13 years 5 months ago
A multivariate prediction model for microarray cross-hybridization
Background: Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental proble...
Yian A. Chen, Cheng-Chung Chou, Xinghua Lu, Elizab...
BMCBI
2005
141views more  BMCBI 2005»
13 years 5 months ago
Construction and validation of the APOCHIP, a spotted oligo-microarray for the study of beta-cell apoptosis
Background: Type 1 diabetes mellitus (T1DM) is a autoimmune disease caused by a long-term negative balance between immune-mediated beta-cell damage and beta-cell repair/regenerati...
Nils E. Magnusson, Alessandra K. Cardozo, Mogens K...