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BMCBI
2007
138views more  BMCBI 2007»
15 years 6 months ago
A full Bayesian hierarchical mixture model for the variance of gene differential expression
Background: In many laboratory-based high throughput microarray experiments, there are very few replicates of gene expression levels. Thus, estimates of gene variances are inaccur...
Samuel O. M. Manda, Rebecca E. Walls, Mark S. Gilt...
BMCBI
2007
106views more  BMCBI 2007»
15 years 6 months ago
Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework
Background: Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Stand...
Michael A. Gilchrist, Hong Qin, Russell L. Zaretzk...
185
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JCB
2007
198views more  JCB 2007»
15 years 6 months ago
Bayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation
Many bioinformatics problems can implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy esti...
Dongxiao Zhu, Alfred O. Hero III
ECCV
2000
Springer
16 years 8 months ago
Model Based Pose Estimator Using Linear-Programming
Given a ? object and some measurements for points in this object, it is desired to find the ? location of the object. A new model based pose estimator from stereo pairs based on l...
Moshe Ben-Ezra, Shmuel Peleg, Michael Werman
BIOCOMP
2008
15 years 7 months ago
Reverse Engineering Module Networks by PSO-RNN Hybrid Modeling
Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reas...
Yuji Zhang, Jianhua Xuan, Benildo de los Reyes, Ro...