about
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatmentPostgenomics: Proteomics and Bioinformatics in Cancer ResearchCan Zipf's law be adapted to normalize microarrays?Dissection of the inflammatory bowel disease transcriptome using genome-wide cDNA microarraysAnalysis of the transcriptome of the protozoan Theileria parva using MPSS reveals that the majority of genes are transcriptionally active in the schizont stage.Testing for differentially expressed genes with microarray data.DAFS: a data-adaptive flag method for RNA-sequencing data to differentiate genes with low and high expressionQuantification of circadian rhythms in single cells.Computational strategies for analyzing data in gene expression microarray experiments.Statistical similarities between transcriptomics and quantitative shotgun proteomics data.A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification.Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tagsThe correlation between architecture and mRNA abundance in the genetic regulatory network of Escherichia coliAdaptive expression responses in the Pol-gamma null strain of S. pombe depleted of mitochondrial genome.The landscape of human proteins interacting with viruses and other pathogens.Inference of gene pathways using mixture Bayesian networks.Impact of missing value imputation on classification for DNA microarray gene expression data--a model-based studyPhysically grounded approach for estimating gene expression from microarray data.Whole-genome positive selection and habitat-driven evolution in a shallow and a deep-sea urchin.Analyzing proteome topology and function by automated multidimensional fluorescence microscopy.Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasetsModeling the next generation sequencing sample processing pipeline for the purposes of classification.Fungal gene expression levels do not display a common mode of distribution.Network succession reveals the importance of competition in response to emulsified vegetable oil amendment for uranium bioremediation.Benford's Law Applies to Online Social NetworksIssues in the analysis of oligonucleotide tiling microarrays for transcript mapping.Elucidating tissue specific genes using the Benford distribution."Hook"-calibration of GeneChip-microarrays: theory and algorithmInformation Conversion, Effective Samples, and Parameter Size.Universality and flexibility in gene expression from bacteria to human.Optimal allocation of replicates for measurement evaluation studiesUniversality in network dynamics.Whole transcriptome organisation in the dehydrated supraoptic nucleus.RNA sequencing reveals two major classes of gene expression levels in metazoan cells.Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.Quantitative proteomic profiling of paired cancerous and normal colon epithelial cells isolated freshly from colorectal cancer patients.Neutral Zone Classifiers Using a Decision-Theoretic Approach With Application to DNA Array Analyses.A new symbolic representation for the identification of informative genes in replicated microarray experiments.A permutation two one-sided tests procedure to detect minimal fold changes of gene expression levels.Agreement of drug discovery data with Benford's law.
P2860
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P2860
description
2002 nî lūn-bûn
@nan
2002 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
Making sense of microarray data distributions.
@ast
Making sense of microarray data distributions.
@en
type
label
Making sense of microarray data distributions.
@ast
Making sense of microarray data distributions.
@en
prefLabel
Making sense of microarray data distributions.
@ast
Making sense of microarray data distributions.
@en
P356
P1433
P1476
Making sense of microarray data distributions.
@en
P2093
David C Hoyle
P304
P356
10.1093/BIOINFORMATICS/18.4.576
P407
P577
2002-04-01T00:00:00Z