共查询到20条相似文献,搜索用时 15 毫秒
1.
2.
Salim Essakali Dennis Carney David Westerman Peter Gambell John F Seymour Alexander Dobrovic 《BMC biotechnology》2008,8(1):6
Background
High purity of tumour samples is a necessity for accurate genetic and expression analysis and is usually achieved by positive selection in chronic lymphocytic leukaemia (CLL). 相似文献3.
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification 总被引:2,自引:0,他引:2
Background
Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. 相似文献4.
5.
Background
Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data. 相似文献6.
Background
The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. 相似文献7.
Background
Androgen acts via androgen receptor (AR) and accurate measurement of the levels of AR protein expression is critical for prostate research. The expression of AR in paired specimens of benign prostate and prostate cancer from 20 African and 20 Caucasian Americans was compared to demonstrate an application of this system. 相似文献8.
9.
Background
The original spotted array technology with competitive hybridization of two experimental samples and measuring relative expression levels is increasingly displaced by more accurate platforms that allow determining absolute expression values for a single sample (for example, Affymetrix GeneChip and Illumina BeadChip). Unfortunately, cross-platform comparisons show a disappointingly low concordance between lists of regulated genes between the latter two platforms. 相似文献10.
Background
Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normalization methods rely on the premise that most genes in compared samples are expressed at similar levels and therefore require accurate identification of nonexpressed genes (additive noise) so that they can be excluded from the normalization procedure. Moreover, key regulatory genes can maintain stringent control of a given response at low expression levels. If arbitrary cutoffs are used for distinguishing expressed from nonexpressed genes, some of these key regulatory genes may be unnecessarily excluded from the analysis. Unfortunately, no accurate method for differentiating additive noise from genes expressed at low levels is currently available. 相似文献11.
Background
Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed. 相似文献12.
Shaleen K Botting Jerome P Trzeciakowski Michelle F Benoit Salama A Salama Concepcion R Diaz-Arrastia 《BMC bioinformatics》2009,10(1):66
Background
We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set. 相似文献13.
Carlos Infante Makoto P Matsuoka Esther Asensio José Pedro Cañavate Michael Reith Manuel Manchado 《BMC molecular biology》2008,9(1):28
Background
Flatfish metamorphosis involves major physiological and morphological changes. Due to its importance in aquaculture and as a model for developmental studies, some gene expression studies have focused on the understanding of this process using quantitative real-time PCR (qRT-PCR) technique. Therefore, adequate reference genes for accurate normalization are required. 相似文献14.
15.
16.
Birte Hellwig Jan G Hengstler Marcus Schmidt Mathias C Gehrmann Wiebke Schormann Jörg Rahnenführer 《BMC bioinformatics》2010,11(1):276
Background
A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions. 相似文献17.
Background
Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls in the original Affymetrix probe annotation have been described; however, few studies have provided rigorous solutions for routine data analysis. 相似文献18.
19.
Rohini Mehta Aybike Birerdinc Noreen Hossain Arian Afendy Vikas Chandhoke Zobair Younossi Ancha Baranova 《BMC molecular biology》2010,11(1):39