A primer on gene expression and microarrays for machine learning researchers

Kuo, Winston Patrick; Kim, Eun Young; Trimarchi, Jeffrey M.; Jenssen, Tor-Kristian; Vinterbo, Staal A.; Ohno-Machado, Lucila

doi:10.1016/j.jbi.2004.07.002

Cited by 36 publications

(32 citation statements)

References 60 publications

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“…Oligonucleotides can be classified into high density microelectronics, macroarray and microarray [30]. Microarrays are based on a glass, plastic or nylon matrix to which specific gene probes are attached in such a manner that complementarity can be obtained between the attached nucleic acid (RNA or DNA) probes and free target complementary nucleic acid labeled with a fluorescent probe, low-intensity or high-intensity fluorescence indicating the low or high expression of a particular gene within a pathosystem or in plants subjected to a specific stress [31,32]. This method allows the simultaneous analysis of thousands of genes of interest, and the identification of both their presence and differential expression, the latter allowing inferences to be made regarding the possible function of specific genes [11].…”

Section: Microarraysmentioning

confidence: 99%

Gene Expression: A Review on Methods for the Study of Defense-Related Gene Differential Expression in Plants

Casassola¹,

Brammer²,

Chaves³

et al. 2013

AJPS

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The plant genes involved in cellular signaling and metabolism have not been fully identified, while the function(s) of many of those which have are as yet incompletely characterized. Gene expression analysis allows the identification of genes and the study of their relationship with cellular processes. There are several options available for studying gene expression, including the use of cDNA and microarray libraries and techniques such as suppression subtractive hybridization (SSH), differential display (DD), RNA fingerprinting by arbitrary primed PCR (RAP), expressed sequence tags (EST), serial analysis of gene expression (SAGE), representational difference analysis (RDA), cDNA-amplified fragment length polymorphism (cDNA-AFLP) and RNA sequencing (RNA-Seq). Focusing on defense-related processes in plants, we present a brief review and examples of each of these methodologies and their advantages and limitations regarding the study of plant gene expression.

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Section: Microarraysmentioning

confidence: 99%

Gene Expression: A Review on Methods for the Study of Defense-Related Gene Differential Expression in Plants

Casassola¹,

Brammer²,

Chaves³

et al. 2013

AJPS

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show abstract

“…For further information about supervised learning algorithms for analyzing gene expression data, the reader is referred to a recent review by Kuo et al (115). Other mathematically more complex supervised methods such as weighted voting (116) and k-nearest neighbors (151,218) are more suited for gene classification and prediction.…”

Section: Analysis Of Gene Expression Datamentioning

confidence: 99%

Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks

Vemuri

Aristidou²

2005

Microbiol Mol Biol Rev

115

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The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding of physiology, but also for practical applications of using biological systems as cell factories. We present some of the recent “-omics” approaches that have expanded our understanding of regulation at the gene, protein, and metabolite level, followed by analysis of the impact of this progress on the advancement of metabolic engineering. Although this review is by no means exhaustive, we attempt to convey our ideology that combining global information from various levels of metabolic hierarchy is absolutely essential in understanding and subsequently predicting the relationship between changes in gene expression and the resulting phenotype. The ultimate aim of this review is to provide metabolic engineers with an overview of recent advances in complementary aspects of regulation at the gene, protein, and metabolite level and those involved in fundamental research with potential hurdles in the path to implementing their discoveries in practical applications

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“…Technologies for measuring gene expression can be broadly divided into two major categories based on the amount of data they produce, namely low and high throughput (Kuo et al, 2004). Low throughput technologies, such as Polymerase Chain Reaction (VanGuilder et al, 2008) and Northern Blot (Lodish et al, 2012), allow expression level measurement for a handful of genes, usually with a high precision.…”

Section: Measuring Gene Expressionmentioning

confidence: 99%

“…Their high precision is counterbalanced, however, by the quantity of genes that can be analyzed. These technologies are, therefore, mostly employed to confirm or reject experimentally previously formulated hypothesis (Kuo et al, 2004;VanGuilder et al, 2008). Technologies that fall under the high throughput category, on the other hand, can provide a complete snapshot of the current state of a cell.…”

Section: Measuring Gene Expressionmentioning

confidence: 99%

On the evaluation of clustering results: measures, ensembles, and gene expression data analysis

Jaskowiak¹

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A primer on gene expression and microarrays for machine learning researchers

Cited by 36 publications

References 60 publications

Gene Expression: A Review on Methods for the Study of Defense-Related Gene Differential Expression in Plants

Gene Expression: A Review on Methods for the Study of Defense-Related Gene Differential Expression in Plants

Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks

On the evaluation of clustering results: measures, ensembles, and gene expression data analysis

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