Gene Expression Data.
Feature selection from gene expression data : molecular signatures for breast cancer prognosis and gene regulation network inference
... of gene regulatory networks (GRN) from a collection of gene expression data has many potential applications, from the elucidation of complex biological processes to the identification of ...
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Mining biological information from 3D gene expression data : the OPTricluster algorithm
... the gene expression data ...noisy data, but also DNA experimental data contains missing ...through data quantization and to recover missing values by imputation ...new ...
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Evaluation and Optimization of Clustering in Gene Expression Data Analysis
... per gene (NDPG), which uses scientific literature to assess whether a group of genes are functionally ...in gene expression levels to assess the reliability of gene clusters identified from ...
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Detect tissue heterogeneity in gene expression data with BioQC
... Finally we assessed tissue heterogeneity in a small subset of GTEx gene expression data by applying BioQC to small- intestine samples (n=40). BioQC revealed three clusters of samples based on ...
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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data
... multi- gene models across 127 ...multi- gene model, whereas the other 66 drugs had the multi-gene model with better precision (see Supplementary File 2 ...multi-variate gene expression ...
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Interpreting fuzzy clustering results with virtual reality-based visual data mining: application to microarray gene expression data
... A. Gene Expression Data from Alzheimer’s disease Alzheimer's disease (AD) is a chronic, progressive, debilitating condition which, along with other neuro-degenerative diseases, represents the largest ...
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The Variable Neighborhood Search Metaheuristic for Fuzzy Clustering cDNA Microarray Gene Expression Data
... ABSTRACT Several thousand genes can be monitored simultaneously using cDNA microarray technology. To exploit the huge amount of information contained in gene expression data, adaptation of existing ...
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Virtual Reality Visual Data Mining with Nonlinear Discriminant Neural Networks: Application to Leukemia and Alzheimer Gene Expression Data
... like gene expression data, where objects are described in terms of several thousands of genes, and classes are often either only separable with nonlinear boundaries, or not separable at ...other ...
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Semi-supervised consensus clustering for gene expression data analysis
... for gene expression datasets is ...labeled data, similarity-based tends to perform ...to gene expression data, but also to other types of data as long as prior knowledge ...
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Mining gene expression data using domain knowledge
... on expression data only, a distance on networks only and a combined dis- ...of gene annotation integration in the cluster- ing ...only expression data while other datasets integrated ...
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Impact of the distance choice on clustering gene expression data using graph decompositions
... of gene interactions is an important research area in ...obtain gene expression ...tomic data, clustering is a rst mandatory step towards a better understanding of the functional ...
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Building Virtual Reality Spaces for Visual Data Mining with Hybrid Evolutionary-Classical Optimization: Application to Microarray Gene Expression Data
... complex data sets, like those coming from microarray gene expression ...raw data and results of data mining algorithms can be obtained using hy- brid optimization methods, thus reducing ...
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GenMiner: mining non-redundant association rules from integrated gene expression data and annotations
... 1 INTRODUCTION Association rule discovery (ARD) is an unsupervised data mining technique for discovering links among sets of variable values (items) from very large datasets. Association rules (AR) identify groups ...
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A Multi-strategy approach to informative gene identification from gene expression data
... employing data mining concepts, refining them and combining them with other existing methods in the field of unsupervised learning to discover useful and more comprehensive information from biological ...
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Deep learning benchmarks on L1000 gene expression data
... We compare traditional classifiers, including feed-forward artificial neural net- works (FF-ANNs), linear methods, random forests, decision trees, and K nearest neighbo[r] ...
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Unsupervised Bayesian linear unmixing of gene expression microarrays
... to gene expression data ...nated gene activity in pattern sets method (CoGAPS), available as an open R-source [12], combines the GAPS- MCMC matrix factorization algorithm with a threshold- ...
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Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome
... BLK expression are associated with several autoimmune diseases via lowering the threshold for B cell activation ( 54 ...downstream expression of cytokines and chemokines and the nuclear factor- κB ...
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Game Theory applied to gene expression analysis
... of gene expression data; one from tumor samples and the other from normal DNA (referent healthy ...an expression ratio largely different from normal samples, and those with expression ...
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Spectral analysis of gene expression profiles using gene networks
... onto gene networks to elucidate the functions perturbed at the level of ...the gene networks could help in the statistical analysis of gene expression data and in their biological ...a ...
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Data mining of gene expression changes in Alzheimer brain
... the Alzheimer and the Normal classes). Section 5.2 describes the method in detail. • P-value and ratio thresholding: In section 5.3, a combination of two- tailed one-sample and 2-sample t-tests assuming unequal variance ...
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