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» MDGA: motif discovery using a genetic algorithm
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BMCBI
2007
112views more  BMCBI 2007»
13 years 7 months ago
Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis
Background: Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene s...
Tae-Min Kim, Yeun-Jun Chung, Mun-Gan Rhyu, Myeong ...
BCBGC
2008
13 years 9 months ago
BMA - Boolean Matrices as Model for Motif Kernels
We introduce the data model BM, which specifies kernels of motifs by means of Boolean matrices. Different from position frequency matrices these only specify which bases can appea...
Jan Schröder, Manfred Schimmler, Heiko Schr&o...
ICDE
2010
IEEE
750views Database» more  ICDE 2010»
14 years 6 days ago
Efficient and accurate discovery of patterns in sequence datasets
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require effici...
Avrilia Floratou, Sandeep Tata, Jignesh M. Patel
CEC
2007
IEEE
13 years 9 months ago
Random search can outperform mutation
— Efficient discovery of lowest level building blocks is a fundamental requirement for a successful genetic algorithm. Although considerable effort has been directed at techniqu...
Cameron Skinner, Patricia J. Riddle
RECOMB
2004
Springer
14 years 8 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, ...