Gene expression datasets
Intelligent Techniques for Gene Expression Datasets
... IV. C ONCLUSION This paper discussed various artificial intelligence based feature selection (BBO, GA, ABC) and classification (KNN, ANN, SVM) techniques. Each of these methods can be used in various situations as needed ...
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Collective analysis of multiple high-throughput gene expression datasets
... high-throughput datasets collectively in order to mine for those findings that are hidden in the aggregation of the datasets in contrast to their ...produced gene expression datasets in ...
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MiningABs: mining associated biomarkers across multi-connected gene expression datasets.
... of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma ...each gene in a group of ABs is required to maintain high cancer sample classification ...
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Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO)
... At present, data sharing and integration of omics data for investigating mechanisms of multifactorial diseases have gained attention. Registration of biological experimental data in public databases has also been ...
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An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
... microarray datasets are selected and grouped based on their prior pathway information from the pathway ...pathway datasets and some genes might be excluded in the ...the gene in gene ...
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clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.
... large gene expression datasets, such as single-cell RNA-Seq datasets, gen- erally results in a large number of clusters, finding biomarkers for the clusters corresponds to testing for ...
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Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets
... Human gene expression datasets show that proposed approach discovers biologically significant ...multiple datasets alleviates the problems associated with biological inference based on a ...
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Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer
... GEO, gene expression omnibus; GGM, Gaussian graphical model; GIS- TIC, genomic identification of significant targets in cancer; GLasso, graphical lasso; INSPIRE, INferring Shared modules from multiPle ...
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Improving Novel Gene Discovery in High-Throughput Gene Expression Datasets
... high-throughput gene expression experiments, the most common first analysis step to discover novel genes is to filter out genes based on their degree of differential expression and the amount of ...
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A Combined Filter Wrapper Classification Method for Gene Selection from Gene Expression Datasets
... microarray, Gene selection, High dimensionality, Gene expression, SVM, ...expressive gene classification has persuaded the researchers on a discovery spree, driving innovations in methods that ...
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Multidimensional partitioning and bi-partitioning : analysis and application to gene expression datasets
... 3 Rectangular Case 3.1 Data and Problem We now consider the case where W ∈ R M × N , with M different to N , in general. As with section 2, we suppose that w ij ≥ 0 represents similarity between objects, but now we think ...
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Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
... of gene expression samples that are much more convenient for expert interpretation, taking gene expression data modeling as a novel challenge for the application of the recently developed ...
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Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxiciology
... new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in ...related, datasets, we find that the dosage level ...
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The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis
... compare gene expression data gener- ated from different experiments, even when using samples from comparable sources that have been processed on the same microarray platform using similar ...enabled ...
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The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis
... compare gene expression data gener- ated from different experiments, even when using samples from comparable sources that have been processed on the same microarray platform using similar ...enabled ...
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brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets
... offers gene prioritisation with accompanying visualisations based on seven gene expression datasets in the post-mortem human brain, the largest such resource ever ...
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Inference of gene regulation from expression datasets
... training datasets, the simulation of the time course data was sensitive to the time intervals of these ...four datasets used in this study, the elutriation dataset (ELU) was collected at a thirty minute ...
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An efficient annotation and gene expression derivation tool for Illumina Solexa datasets
... Concatenation of reads TASE analysis is divided into two distinct but yet highly related phases: DNA read concatenation for each given chromosome per selected lane of interest, followed by gene expression ...
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Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets
... probesets expression data with the Robust Multiarray Average (RMA) algorithm [22], which combines convolution background correction, quantile normal- ization and median-polish-based multiarray summarization, as ...
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A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
... Specifically, for chromatin features profiled by ChIP- chip experiments, the signals of the probes that fall into a bin were averaged. For features profiled by ChIP-seq experiments, the number of reads that cover a bin ...
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