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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書目名稱Gene Function Analysis影響因子(影響力)




書目名稱Gene Function Analysis影響因子(影響力)學科排名




書目名稱Gene Function Analysis網絡公開度




書目名稱Gene Function Analysis網絡公開度學科排名




書目名稱Gene Function Analysis被引頻次




書目名稱Gene Function Analysis被引頻次學科排名




書目名稱Gene Function Analysis年度引用




書目名稱Gene Function Analysis年度引用學科排名




書目名稱Gene Function Analysis讀者反饋




書目名稱Gene Function Analysis讀者反饋學科排名

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Association Analysis for Large-Scale Gene Set Dataefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pa

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peritonitis

Albumin

Prediction of Intrinsic Disorder and Its Use in Functional Proteomicsation of a given protein, looking for several key features. However, ID proteins with their dynamic structures that interconvert on a number of time-scales are difficult targets for the majority of traditional biophysical and biochemical techniques. Structural and functional analyses of these protei

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collateral

Estimating Protein Function Using Protein-Protein Relationshipsins. In such a scenario, the context of the proteins’ functional associations can be used for annotation; overrepresented functional linkages with a certain class of proteins or members of a pathway allow putative function assignments based on the “guilt-by-association” principle. Two computational

Pessary

ECG769

Mining Biomedical Data Using MetaMap Transfer (MMTx) and the Unified Medical Language System (UMLS) in the Unified Medical Language System using MetaMap Transfer (MMTx). MMTx can be used in applications including mining and inferring relationship between concepts in MEDLINE publications by transforming free text into computable concepts. MMTx is in general not designed to be an end-user program;

parsimony

Statistical Methods for Identifying Differentially Expressed Gene Combinations expression regulatory networks. Statistically, the identification of these changes can be viewed as a search for groups (most typically pairs) of genes whose expression provides better phenotype discrimination when considered jointly than when considered individually. Such groups are defined as bei