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Titlebook: Gene Function Analysis; Michael F. Ochs Book 2007 Humana Press 2007 DNA.Mutant.Promoter.Proteomics.Stefan Dübel.bioinformatics.gene expres

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Estimating Gene Function With Least Squares Nonnegative Matrix Factorizations estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized .., rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.
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Mining Biomedical Data Using MetaMap Transfer (MMTx) and the Unified Medical Language System (UMLS)therefore, a simple analysis is described using MMTx for users without any programming knowledge. In addition, two Java template files are provided for automated processing of the output from MMTx and users can adopt this with minimum Java program experience.
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Gene Function Analysis Using the Chicken B-Cell Line DT40tep standardized protocol to creating a gene knockout mutant in DT40. With careful consideration, the methods and protocols described herein can be easily modified to allow for further gene manipulations such as creating a knockin or a conditional mutant.
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Statistical Methods for Identifying Differentially Expressed Gene Combinationses whose expression provides better phenotype discrimination when considered jointly than when considered individually. Such groups are defined as being jointly differentially expressed. In this chapter several approaches for identifying jointly differentially expressed groups of genes are reviewed of compared on a set of simulations.
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Sybil: Methods and Software for Multiple Genome Comparison and Visualizationle techniques provide the user of the Sybil software (The Institute for Genomic Research [TIGR] Bioinformatics Department) with a browsable graphical display of his or her “input” genomes, showing which genes are conserved based on the parameters supplied to the protein clustering algorithm. For any
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