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» Predicting Nucleolar Proteins Using Support-Vector Machines
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BMCBI
2007
126views more  BMCBI 2007»
13 years 7 months ago
High-throughput identification of interacting protein-protein binding sites
Background: With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Ther...
Jo-Lan Chung, Wei Wang, Philip E. Bourne
CIKM
2008
Springer
13 years 9 months ago
Learning a two-stage SVM/CRF sequence classifier
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Guilherme Hoefel, Charles Elkan
GCB
2010
Springer
204views Biometrics» more  GCB 2010»
13 years 5 months ago
Learning Pathway-based Decision Rules to Classify Microarray Cancer Samples
: Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninfor...
Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasn...
BMCBI
2006
127views more  BMCBI 2006»
13 years 7 months ago
Automatic discovery of cross-family sequence features associated with protein function
Background: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomic...
Markus Brameier, Josien Haan, Andrea Krings, Rober...
BMCBI
2006
120views more  BMCBI 2006»
13 years 7 months ago
Optimizing amino acid substitution matrices with a local alignment kernel
Background: Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, cal...
Hiroto Saigo, Jean-Philippe Vert, Tatsuya Akutsu