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BMCBI
2005
107views more  BMCBI 2005»
13 years 7 months ago
Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multip
Background: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in...
Jiren Wang, Wing-Kin Sung, Arun Krishnan, Kuo-Bin ...
BMCBI
2004
114views more  BMCBI 2004»
13 years 7 months ago
Profiled support vector machines for antisense oligonucleotide efficacy prediction
Background: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises ...
Gustavo Camps-Valls, Alistair M. Chalk, Antonio J....
ICANN
2001
Springer
14 years 5 days ago
Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
Thomas Frontzek, Thomas Navin Lal, Rolf Eckmiller
BMCBI
2006
106views more  BMCBI 2006»
13 years 7 months ago
Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector
Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their...
H. H. Lin, L. Y. Han, H. L. Zhang, C. J. Zheng, B....
ML
2002
ACM
220views Machine Learning» more  ML 2002»
13 years 7 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich