Results 11 to 20 of about 3,887,902 (293)
Finding Groups in Gene Expression Data [PDF]
BioMed Research International, 2005 The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large‐scale, high‐throughput investigations of gene activity and have
Hand, DJ, Heard, NA
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Error Distribution for Gene Expression Data [PDF]
Statistical Applications in Genetics and Molecular Biology, 2005 We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric bootstrap leads to more powerful tests as we show that the t ...
Purdom, Elizabeth, Holmes, Susan P.
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Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification [PDF]
Vietnam Journal of Computer Science, 2019 The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an ...
Joanna Jȩdrzejowicz +2 more
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The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases
Molecular Systems Biology, 2020 Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts.
Noam Auslander +7 more
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BMC Genomics, 2017 Background High-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease ...
Vladimir A. Kuznetsov +2 more
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Seed-based biclustering of gene expression data. [PDF]
PLoS ONE, 2012 BACKGROUND: Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental
Jiyuan An +2 more
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Gene Expression Commons: an open platform for absolute gene expression profiling. [PDF]
, 2012 Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments.
Bhattacharya, Deepta +9 more
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Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. [PDF]
PLoS Computational Biology, 2015 Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies.
Boris P Hejblum +2 more
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, 2008 This report contains a gene expression summary of the oculomotor nucleus, derived from the Allen Brain Atlas (ABA) in situ hybridization mouse data set.
Allen Institute for Brain Science +3 more
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An Archive-based Steady-State Fuzzy Differential Evolutionary Algorithm for Data Clustering (ASFDEaDC) [PDF]
Journal of Information Technology Management, 2021 In the current paper, we have assimilated fuzzy techniques and optimization techniques, namely differential evolution, to put forward a modern archive-based fuzzy evolutionary algorithm for multi-objective optimization using clustering.
Nushrat Praveen, Vally D
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