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    BMCBI
    2006
    119views more  BMCBI 2006»
    Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip stu
    13 years 7 months ago
    Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip stu
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    Background: The choice of probe set algorithms for expression summary in a GeneChip study has a great impact on subsequent gene expression data analysis. Spiked-in cRNAs with know...
    Zihua Hu, Gail R. Willsky
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    BMCBI
    2006
    112views more  BMCBI 2006»
    GENOMEMASKER package for designing unique genomic PCR primers
    13 years 7 months ago
    GENOMEMASKER package for designing unique genomic PCR primers
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    Background: The design of oligonucleotides and PCR primers for studying large genomes is complicated by the redundancy of sequences. The eukaryotic genomes are particularly diffic...
    Reidar Andreson, Eric Reppo, Lauris Kaplinski, Mai...
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    BMCBI
    2006
    127views more  BMCBI 2006»
    Automatic discovery of cross-family sequence features associated with protein function
    13 years 7 months ago
    Automatic discovery of cross-family sequence features associated with protein function
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    Background: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomic...
    Markus Brameier, Josien Haan, Andrea Krings, Rober...
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    BMCBI
    2006
    112views more  BMCBI 2006»
    Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins
    13 years 7 months ago
    Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins
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    Background: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such...
    Pantelis G. Bagos, Theodore D. Liakopoulos, Stavro...
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    BMCBI
    2006
    146views more  BMCBI 2006»
    Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
    13 years 7 months ago
    Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
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    Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
    Michael Meissner, Michael Schmuker, Gisbert Schnei...
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