Comparative Enumeration Gene Expression
نویسندگان
چکیده
منابع مشابه
Comparative Enumeration Gene Expression
This paper is about differential gene expression measured by transcript counting methods such as SAGE or MPSS. It introduces two significance tests for detection of differential expressed tags: frequentist and Bayesian. Under the frequentist view, it is proposed a test that computes the critical level as a function of each tag total frequency. Under the Bayesian view the Full Bayesian Significa...
متن کاملComparative gene expression of intestinal metabolizing enzymes.
The purpose of this study was to compare the expression profiles of drug-metabolizing enzymes in the intestine of mouse, rat and human. Total RNA was isolated from the duodenum and the mRNA expression was measured using Affymetrix GeneChip oligonucleotide arrays. Detected genes from the intestine of mouse, rat and human were ca. 60% of 22690 sequences, 40% of 8739 and 47% of 12559, respectively...
متن کاملSimplifying Gene Expression Microarray Comparative Analysis
Gene Expression Comparative Analysis allows bioinformatics researchers to discover the conserved or specific functional regulation of genes. This is achieved through comparisons between quantitative gene expression measurements obtained in different species on different platforms to address a particular biological system. Comparisons are made more difficult due to the need to map orthologous ge...
متن کاملSimultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data
Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-spe...
متن کاملA comparative analysis of biclustering algorithms for gene expression data
The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nature Precedings
سال: 2008
ISSN: 1756-0357
DOI: 10.1038/npre.2008.2002.1