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» Using Multiple Alignments to Improve Gene Prediction
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132
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BMCBI
2006
173views more  BMCBI 2006»
15 years 3 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
103
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BMCBI
2005
82views more  BMCBI 2005»
15 years 3 months ago
A novel method for prokaryotic promoter prediction based on DNA stability
Background: In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step to...
Aditi Kanhere, Manju Bansal
BMCBI
2007
233views more  BMCBI 2007»
15 years 3 months ago
160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)
Background: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching s...
Isaac T. S. Li, Warren Shum, Kevin Truong
141
Voted
BMCBI
2006
108views more  BMCBI 2006»
15 years 3 months ago
Gene function classification using Bayesian models with hierarchy-based priors
We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic ...
Babak Shahbaba, Radford M. Neal
114
Voted
BMCBI
2006
124views more  BMCBI 2006»
15 years 3 months ago
Network-based de-noising improves prediction from microarray data
Background: Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as...
Tsuyoshi Kato, Yukio Murata, Koh Miura, Kiyoshi As...