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VLSISP
1998
111views more  VLSISP 1998»
14 years 9 months ago
Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures
This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification fo...
Yue Wang, Tülay Adali, Chi-Ming Lau, Sun-Yuan...
ICIP
2001
IEEE
15 years 11 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
ICASSP
2010
IEEE
14 years 9 months ago
Swift: Scalable weighted iterative sampling for flow cytometry clustering
Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets...
Iftekhar Naim, Suprakash Datta, Gaurav Sharma, Jam...
BMCBI
2011
14 years 1 months ago
Genotype calling in tetraploid species from bi-allelic marker data using mixture models
Background: Automated genotype calling in tetraploid species was until recently not possible, which hampered genetic analysis. Modern genotyping assays often produce two signals, ...
Roeland E. Voorrips, Gerrit Gort, Ben Vosman
71
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WABI
2009
Springer
124views Bioinformatics» more  WABI 2009»
15 years 4 months ago
Mimosa: Mixture Model of Co-expression to Detect Modulators of Regulatory Interaction
Background: Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often use...
Matthew Hansen, Logan Everett, Larry Singh, Sridha...