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» Gene Expression Clustering with Functional Mixture Models
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ISMB
2000
13 years 11 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
2008
101views more  BMCBI 2008»
13 years 10 months ago
Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in dros
Background: Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene O...
Wensheng Zhang, Sige Zou, Jiuzhou Song
BMCBI
2008
144views more  BMCBI 2008»
13 years 10 months ago
WGCNA: an R package for weighted correlation network analysis
Background: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method ...
Peter Langfelder, Steve Horvath
NIPS
2004
13 years 11 months ago
Joint Probabilistic Curve Clustering and Alignment
Clustering and prediction of sets of curves is an important problem in many areas of science and engineering. It is often the case that curves tend to be misaligned from each othe...
Scott Gaffney, Padhraic Smyth
MICCAI
2009
Springer
14 years 11 months ago
Non-rigid Registration of High Angular Resolution Diffusion Images Represented by Gaussian Mixture Fields
In this paper, we present a novel algorithm for non-rigidly registering two high angular resolution diffusion weighted MRIs (HARDI), each represented by a Gaussian mixture field (G...
Baba C. Vemuri, Guang Cheng 0002, Paul R. Carney, ...