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» Predicting Nucleolar Proteins Using Support-Vector Machines
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147
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BIOINFORMATICS
2007
151views more  BIOINFORMATICS 2007»
15 years 3 months ago
A new protein-protein docking scoring function based on interface residue properties
Motivation: Protein–protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low s...
Julie Bernauer, Jérôme Azé, Jo...
113
Voted
BMCBI
2007
129views more  BMCBI 2007»
15 years 3 months ago
Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach
Background: Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need...
Carson M. Andorf, Drena Dobbs, Vasant Honavar
128
Voted
CEC
2009
IEEE
15 years 10 months ago
Evolving hypernetwork models of binary time series for forecasting price movements on stock markets
— The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order...
Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian,...
149
Voted
BIOINFORMATICS
2005
140views more  BIOINFORMATICS 2005»
15 years 3 months ago
Profile-based direct kernels for remote homology detection and fold recognition
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. Supervised learning algorithms based on support vector machines are ...
Huzefa Rangwala, George Karypis
139
Voted
CIKM
2008
Springer
15 years 5 months ago
The role of syntactic features in protein interaction extraction
Most approaches for protein interaction mining from biomedical texts use both lexical and syntactic features. However, the individual impact of these two kinds of features on the ...
Timur Fayruzov, Martine De Cock, Chris Cornelis, V...