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    BMCBI
    2005
    143views more  BMCBI 2005»
    Gene capture prediction and overlap estimation in EST sequencing from one or multiple libraries
    13 years 5 months ago
    Gene capture prediction and overlap estimation in EST sequencing from one or multiple libraries
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    Background: In expressed sequence tag (EST) sequencing, we are often interested in how many genes we can capture in an EST sample of a targeted size. This information provides ins...
    Ji-Ping Z. Wang, Bruce G. Lindsay 0002, Liying Cui...
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    BMCBI
    2008
    130views more  BMCBI 2008»
    Function approximation approach to the inference of reduced NGnet models of genetic networks
    13 years 5 months ago
    Function approximation approach to the inference of reduced NGnet models of genetic networks
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    Background: The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of m...
    Shuhei Kimura, Katsuki Sonoda, Soichiro Yamane, Hi...
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    BMCBI
    2006
    119views more  BMCBI 2006»
    Hidden Markov Model Variants and their Application
    13 years 5 months ago
    Hidden Markov Model Variants and their Application
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    Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
    Stephen Winters-Hilt
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    BMCBI
    2004
    180views more  BMCBI 2004»
    Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
    13 years 5 months ago
    Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
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    Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identi...
    Virginie M. Aris, Michael J. Cody, Jeff Cheng, Jam...
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    CSB
    2005
    IEEE
    189views Bioinformatics» more  CSB 2005»
    Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
    13 years 11 months ago
    Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
    Download  conferences.computer.org
    We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
    Xutao Deng, Huimin Geng, Hesham H. Ali
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