Microarray data
Facilitating functional annotation of chicken microarray data
... S9 Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data. Bart HJ van den Berg[r] ...
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Improved elucidation of biological processes linked to diabetic nephropathy by single probe-based microarray data analysis.
... for microarray data that consists of four steps: single probe-transcript annotation, total intensity normalization, SAM analysis (adapted to single probe handling) and transcript identification based on ...
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Comparison and consolidation of microarray data sets of human tissue expression
... Remarkably, we found that the detected tissue-specific expression is strongly influenced by the choice of microarray platform. The observed divergence could have multiple reasons. Foremost, the set of tissue ...
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Use of signal thresholds to determine significant changes in microarray data analyses
... plots, data filtration and the determination of 95% confidence in- tervals for each of the signal intensity ...any microarray data analysis, and our visual analysis showed three important ...
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Feature Selection using Complementary Particle Swarm Optimization for DNA Microarray Data
... Abstract—DNA microarray data had been used to help the analysis of cancer and disease. Feature selection was an important dimensionality reduction technique in DNA microarray. The huge combinations ...
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Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.
... phenotypic microarray data with novel procedures for grouping, normalization and effect ...experimental data, ...tilevel data are effectively processed by a hierarchical model in the Bayesian ...
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Integrated analysis of microarray data of atherosclerotic plaques: modulation of the ubiquitin-proteasome system.
... of microarray data The data of mRNA and miRNA microarray were filtered and normalized, and then the differentially expressed miRNAs/ mRNAs (corrected ...
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A kernel-based multivariate feature selection method for microarray data classification.
... because of their low complexity and fast performance for high dimensionality of microarray data analyses. However, some valuable genes discarded by univariate methods may have great contribution for ...
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biDCG: a new method for discovering global features of DNA microarray data via an iterative re-clustering procedure.
... analyzing microarray data either clustered genes only based on expression patterns [11], or clustered genes and samples independently, with the expression data matrix being subsequently reorganized ...
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Systematical detection of significant genes in microarray data by incorporating gene interaction relationship in biological systems.
... DNA microarray technologies have been widely used in biological studies, and simultaneously measure expression levels of thousands of genes across cells or tissues under different conditions ...the ...
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A Hybrid Reduction Approach for Enhancing Cancer Classification of Microarray Data
... of microarray data based on two ML gene ranking techniques (T- test and ...mining microarray gene expression profiles. Four public cancer microarray databases were used for evaluating the ...
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LEAF: Leave-one-out Forward Selection Method for Gene Selection in DNA Microarray Data
... Recent progress in bioinformatics technology has facili- tated large-scale screening for candidate biomarkers [6]. A biomarker, as the name implies, is a cell-derived substance such as a gene, protein or enzyme that can ...
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An Analytical Process for Screening Susceptibility Genes of Type 2 Diabetes Mellitus Using Pooled DNA Microarray Data
... separate microarray. Each microarray was scanned using the GeneChip® scanner and GeneChip® Operating software (GCOS) ...pooled data of control, four pools data of non-obese T2DM and three ...
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LEAF: Leave-one-out Forward Selection Method for Informative Gene Discovery in DNA Microarray Data
... Recent progress in bioinformatics technology has facili- tated large-scale screening for candidate biomarkers [6]. A biomarker, as the name implies, is a cell-derived substance such as a gene, protein or enzyme that can ...
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MIDClass: microarray data classification by association rules and gene expression intervals.
... We compared our system against competitors using a Leave- One-Out-Cross-Validation (LOOCV). Using cross-validation one can better assess the performance of the classifier and predict how the classifier will generalize to ...
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Density based pruning for identification of differentially expressed genes from microarray data
... Computational Biology Program, George Mason University, Virginia, USA; Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Austria; BioMedical Informatics [r] ...
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Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
... This BMC Bioinformatics supplement issue consists of 27 peer-reviewed papers selected from IMSCCS Symposium of Computational Biology and Bioinformatics (SCBB) held in Zhejiang University, Hangzhou, China on June 21-22, ...
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Outcome-Driven Cluster Analysis with Application to Microarray Data.
... One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for ...
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Discovering biological progression underlying microarray samples.
... analyze microarray data include unsupervised clustering [19,24], supervised classification [25,26,27,28], and statistical tests for differential expression ...[8,9,10], microarray time series ...
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Integrated analysis of differential miRNA and mRNA expression profiles in human radioresistant and radiosensitive nasopharyngeal carcinoma cells.
... of microarray data was validated for randomly selected eight miRNAs and nine genes; (2) 174 miRNA target were identified, and most of their functions and regulating pathways were related to tumor ...
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