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» Gene Expression Clustering with Functional Mixture Models
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GECCO
2005
Springer
146views Optimization» more  GECCO 2005»
14 years 3 months ago
An empirical study of the robustness of two module clustering fitness functions
Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust...
Mark Harman, Stephen Swift, Kiarash Mahdavi
BMCBI
2008
124views more  BMCBI 2008»
13 years 10 months ago
A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays
Background: Microarray pre-processing usually consists of normalization and summarization. Normalization aims to remove non-biological variations across different arrays. The norm...
Huanying Ge, Chao Cheng, Lei M. Li
IVC
2006
154views more  IVC 2006»
13 years 10 months ago
Manifold based analysis of facial expression
We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embed...
Ya Chang, Changbo Hu, Rogerio Feris, Matthew Turk
BMCBI
2010
164views more  BMCBI 2010»
13 years 7 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
SAC
2004
ACM
14 years 3 months ago
Time-frequency feature detection for time-course microarray data
Gene clustering based on microarray data provides useful functional information to the working biologists. Many current gene-clustering algorithms rely on Euclidean-based distance...
Jiawu Feng, Paolo Emilio Barbano, Bud Mishra