[PDF] Top 20 Pairwise gene GO-based measures for biclustering of high-dimensional expression data
Has 10000 "Pairwise gene GO-based measures for biclustering of high-dimensional expression data" found on our website. Below are the top 20 most common "Pairwise gene GO-based measures for biclustering of high-dimensional expression data".
Pairwise gene GO-based measures for biclustering of high-dimensional expression data
... these high-dimensional datasets, it can be firstly observed that the simGIC measure intro- duces a bias during the search process, and as a consequence, the scatter search algorithm improves giving rise to ... See full document
19
BicPAM: Pattern-based biclustering for biomedical data analysis
... HCLS1 gene that plays a key role in regulating clonal expan- sion and deletion in lymphoid cells [85], IRF1 protein that acts as a tumor suppressor and plays a role not only in antagonism of tumor cell growth but ... See full document
30
Validation of reference genes for quantitative real time PCR studies in the dentate gyrus after experimental febrile seizures
... reference gene quantities that are expressed relative to the sample with the highest quantity, served as data input for geNorm [9] or Normfinder ...of gene expression stability (M value) and ... See full document
8
An Extensive Survey On Biclustering Approaches And Algorithms For Gene Expression Data
... the gene expression ...biclusters based on the maximal acceptable score of the MSR (Mean Squared Residue) evaluation ...from gene expression ...Rho) measures function. Using the ... See full document
9
Greedy Two Way K-Means Clustering For Optimal Coherent Triclsuter
... the gene expression data is very ...the gene expression data comprises of thousands of genes with experimental conditions over time point ...Microarray gene ... See full document
6
A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series
... series expression data analysis, that finds and reports all maxi- mal contiguous column coherent biclusters with approxi- mate expression patterns in time polynomial in the size of the ... See full document
39
A New Test for Large Dimensional Regression Coefficients
... Some high dimensional data, such as gene expression datasets in microarray, exhibits the property that the number of covariates greatly exceeds the sample ...discrepancy measures ... See full document
5
BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
... conditions based on rela- tions inferred from data, relying strictly on computational ...overrepresented GO terms. In a first step, GO annotations are extracted requiring two distinct files ... See full document
11
SEURAT: Visual analytics for the integrated analysis of microarray data
... the gene annotations and clinical data. The gene expression matrix is visualized by a heatmap, where the gene expression levels are represented by col- ...the gene ... See full document
6
A Weighted Mutual Information Biclustering Algorithm for Gene Expression Data
... used biclustering algo- rithm, our algorithm can produce better quality biclusters from gene dataset, although the mean square residue of our experimental result is relatively larger than DBF algorithm, but ... See full document
18
Biclustering of Gene Expression Data by Correlation-Based Scatter Search
... a high number of genes and biclusters with only a reduced group of genes due to the high standard deviation of the number of ...a GO term as ...each GO category or have a study fraction less ... See full document
17
Configurable pattern-based evolutionary biclustering of gene expression data
... existing biclustering approaches base their search for biclusters on evaluation ...of biclustering tools that follow different strate- gies and algorithmic concepts which guide the search towards meaningful ... See full document
22
K Means Based Clustering In High Dimensional Data
... the data space. High dimensional data does not cluster large ...lower dimensional subspaces are easily ...clustering high-dimensional data. Based on a ... See full document
5
Application of simulated annealing to the biclustering of gene expression data
... Gene expression datasets are continually growing in size as more experiments are carried out, and as experimental capac- ity ...the expression of genes to be highly similar under one set of ... See full document
7
Biclustering for Microarray Data: A Short and Comprehensive Tutorial
... from biclustering are as follows: i) clustering methods can be applied to either the rows or the columns of the data matrix in separately, where as in biclustering methods, it performs clustering in ... See full document
5
Gene Selection and Classification Using Linear Support Vector Machine Based On Microarray Data
... using Gene Expression Data. However, micro array expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of ... See full document
6
Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data
... revealed high extent of heterogene- ity among samples in a disease, which collectively emphasize the benefits of our analysis for the identification of the novel molecular mechanisms underlying multiple brain ... See full document
8
Cluster based boosting for high dimensional data
... [3] When less noisy data is present AdaBoost rarely suffers the issues of over fitting. The adaptive boosting algorithm known as AdaBoost provided great success and proved as important developments in ... See full document
5
GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion
... With the rapid development of RNA high-throughput technologies, increasing number of diseases related circRNAs are discovered. Therefore, people pay more attention to revealing the intricate relationships between ... See full document
14
A temporal precedence based clustering method for gene expression microarray data
... Granger causality test is not restricted to only linear models, and it can be readily extended to include non- linear terms in case we observe any non-linear behavior in the data. Some examples of non-linear ... See full document
26
Related subjects