CoXpress: differential co-expression in gene expression data [PDF]
BMC Bioinformatics, 2006 Background Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset.
Watson Michael
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Identifying gene interaction enrichment for gene expression data. [PDF]
PLoS ONE, 2009 Gene set analysis allows the inclusion of knowledge from established gene sets, such as gene pathways, and potentially improves the power of detecting differentially expressed genes.
Jigang Zhang, Jian Li, Hong-Wen Deng
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Gene set analysis for longitudinal gene expression data [PDF]
BMC Bioinformatics, 2011 Background Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations.
Piepho Hans-Peter +5 more
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Bayesian biclustering of gene expression data [PDF]
BMC Genomics, 2008 Background Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions ...
Liu Jun S, Gu Jiajun
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Gene Expression Databases and Data Mining [PDF]
BioTechniques, 2003 The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study.
Pascale Anderle +7 more
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Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification [PDF]
Frontiers in Artificial IntelligenceThere is a great need to categorize cancer types for early cancer detection and treatment. RNA-Seq data is essential for getting insight into the differentially expressed genes. Due to its high dimensionality and complexity, performing an analysis on RNA-
Waqas Haider Bangyal +6 more
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Inference of gene networks using gene expression data with applications [PDF]
HeliyonGene networks (GNs) use graphs to represent the interaction relationships between genes. Large-scale GNs are often sparse and contain hub genes that interact with many other genes.
Chi-Kan Chen
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Meta-Analysis of RNA-Seq and Microarray Expression Data to Identify Genes Effective in Sheep Muscle Growth and Development [PDF]
پژوهشهای علوم دامی ایران, 2023 Introduction: Among different sheep breeds in the world, the Texel breed is known as a meaty and muscular breed. Skeletal muscle growth is a step-by-step and exponential process from differentiation, development and maturation, which is regulated by gene
Fahime Mohammadi +2 more
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Characterization of RNA in exosomes secreted by human breast cancer cell lines using next-generation sequencing [PDF]
PeerJ, 2013 Exosomes are nanosized (30–100 nm) membrane vesicles secreted by most cell types. Exosomes have been found to contain various RNA species including miRNA, mRNA and long non-protein coding RNAs. A number of cancer cells produce elevated levels of exosomes.
Piroon Jenjaroenpun +5 more
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Gene expression data preprocessing [PDF]
Bioinformatics, 2003 Abstract Summary: We present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat ...
J, Herrero, R, Díaz-Uriarte, J, Dopazo
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