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» Using Multiple Alignments to Improve Gene Prediction
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ISMB
2000
13 years 8 months ago
A Probabilistic Learning Approach to Whole-Genome Operon Prediction
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this ...
Mark Craven, David Page, Jude W. Shavlik, Joseph B...
BMCBI
2007
164views more  BMCBI 2007»
13 years 7 months ago
SEARCHPATTOOL: a new method for mining the most specific frequent patterns for binding sites with application to prokaryotic DNA
Background: Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limite...
Fathi Elloumi, Martha Nason
BMCBI
2007
226views more  BMCBI 2007»
13 years 7 months ago
MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
Background: MicroRNAs (miRNAs) are recognized as one of the most important families of noncoding RNAs that serve as important sequence-specific post-transcriptional regulators of ...
Ting-Hua Huang, Bin Fan, Max F. Rothschild, Zhi-Li...
BMCBI
2005
198views more  BMCBI 2005»
13 years 7 months ago
Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural
Background: The sequencing of the human genome has enabled us to access a comprehensive list of genes (both experimental and predicted) for further analysis. While a majority of t...
Qicheng Ma, Gung-Wei Chirn, Richard Cai, Joseph D....
BMCBI
2006
148views more  BMCBI 2006»
13 years 7 months ago
Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores
Background: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epi...
Jesper Salomon, Darren R. Flower