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Methods in Molecular Biology (Clifton,... 2019A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination... (Review)
Review
A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.
Topics: Algorithms; Microarray Analysis; ROC Curve
PubMed: 31115892
DOI: 10.1007/978-1-4939-9442-7_11 -
Pediatric Endocrinology Reviews : PER Sep 2015Chromosomal microarray analysis (CMA) is a technology used for the detection of clinically-significant microdeietions or duplications, with a high sensitivity for... (Review)
Review
Chromosomal microarray analysis (CMA) is a technology used for the detection of clinically-significant microdeietions or duplications, with a high sensitivity for submicroscopic aberrations. It is able to detect changes as small as 5-10Kb in size - a resolution up to 1000 times higher than that of conventional karyotyping. CMA is used for uncovering copy number variants (CNVs) thought to play an important role in the pathogenesis of a variety of disorders, primarily neurodevelopmental disorders and congenital anomalies. CMA may be applied in the prenatal or postnatal setting, with unique benefits and limitations in each setting. The growing use of CMA makes it essential for practicing physicians to understand the principles of this technology and be aware of its powers and limitations.
Topics: Adult; Chromosomes; Female; Genetic Testing; Humans; Infant, Newborn; Karyotyping; Microarray Analysis; Pregnancy; Prenatal Diagnosis
PubMed: 26540760
DOI: No ID Found -
Methods in Molecular Biology (Clifton,... 2022Glycan microarrays provide a high-throughput technology for rapidly profiling interactions between carbohydrates and glycan-binding proteins (GBPs). Use of glycan...
Glycan microarrays provide a high-throughput technology for rapidly profiling interactions between carbohydrates and glycan-binding proteins (GBPs). Use of glycan microarrays involves several general steps, including construction of the microarray, carrying out the assay, detection of binding events, and analysis of the results. While multiple platforms have been developed to construct microarrays, most utilize fluorescence for detection of binding events. This chapter describes methods to acquire and process microarray images, including generating GAL files, imaging of the slide, aligning the grid, detecting problematic spots, and evaluating the quality of the data. The chapter focuses on processing our neoglycoprotein microarrays, but many of the lessons we have learned are applicable to other array formats.
Topics: Carbohydrates; Carrier Proteins; Microarray Analysis; Polysaccharides
PubMed: 34972931
DOI: 10.1007/978-1-0716-2148-6_5 -
Current Opinion in Chemical Biology Oct 2009Glycosylation, a ubiquitous post-translational modification of proteins and lipids that generates enormous functional diversity, is rapidly gaining attention in the... (Review)
Review
Glycosylation, a ubiquitous post-translational modification of proteins and lipids that generates enormous functional diversity, is rapidly gaining attention in the postgenomic era. The systematic study of glycans, that is glycomics, has been driven by the development of new analytical tools well suited to the inherent complexities of carbohydrate analysis, such as lectin-based microarray technologies. Recent work has demonstrated the utility of these analytical tools for glycomics in both clinical and research settings, for example identifying novel biomarkers associated with disease progression or studying HIV-1 exit mechanisms. This review highlights these new lectin-based microarray technologies and their role in the emerging field of glycomics.
Topics: Animals; Glycomics; Glycoproteins; Humans; Lectins; Microarray Analysis; Polysaccharides
PubMed: 19716334
DOI: 10.1016/j.cbpa.2009.07.013 -
Briefings in Functional Genomics &... Dec 2007Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target... (Review)
Review
Microarray based transcription profiling is now a consolidated methodology and has widespread use in areas such as pharmacogenomics, diagnostics and drug target identification. Large-scale microarray studies are also becoming crucial to a new way of conceiving experimental biology. A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. More recently, the focus has switched to algorithms and database development for microarray data mining. Furthermore, the evolution of microarray technology is allowing researchers to grasp the regulative nature of transcription, integrating basic expression analysis with mRNA characteristics, i.e. exon-based arrays, and with DNA characteristics, i.e. comparative genomic hybridization, single nucleotide polymorphism, tiling and promoter structure. In this article, we will review approaches used to detect differentially expressed genes and to link differential expression to specific biological functions.
Topics: Animals; Gene Expression Profiling; Microarray Analysis; Quality Control; Statistics as Topic
PubMed: 18216026
DOI: 10.1093/bfgp/elm034 -
Methods in Molecular Biology (Clifton,... 2019The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray...
The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray technologies and high-throughput technologies is considered under a data analysis view. The up-to-date limitations of DNA microarrays are important to forecast challenges and future trends in microarray data analysis; these include data analysis techniques associated with an increasing sample sizes, new feature selection methods, deep learning techniques, covariate significance testing as well as false discovery rate methods, among other procedures for a better interpretability of the results.
Topics: Algorithms; Deep Learning; Humans; Microarray Analysis
PubMed: 31115895
DOI: 10.1007/978-1-4939-9442-7_14 -
Genomics Jul 2019High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be... (Comparative Study)
Comparative Study
High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.
Topics: Animals; Gene Expression Profiling; Humans; Microarray Analysis; Signal Transduction; Software; Time
PubMed: 29614346
DOI: 10.1016/j.ygeno.2018.03.021 -
Methods in Enzymology 2006Microarrays have revolutionized molecular biology and enabled biologists to perform global analysis on the expression of tens of thousands of genes simultaneously. They... (Review)
Review
Microarrays have revolutionized molecular biology and enabled biologists to perform global analysis on the expression of tens of thousands of genes simultaneously. They have been widely used in gene discovery, biomarker determination, disease classification, and studies of gene regulation. Microarrays have been applied in stem cell research to identify major features or expression signatures that define stem cells and characterize their differentiation programs toward specific lineages. Here we provide a review of the microarray technology, including the introduction of array platforms, experimental designs, RNA isolation and amplification, array hybridization, and data analysis. We also detail examples that apply microarray technology to address several of the main questions in stem cell biology.
Topics: Animals; Cell Differentiation; Humans; Microarray Analysis; Nucleic Acid Amplification Techniques; RNA; Stem Cells
PubMed: 17161699
DOI: 10.1016/S0076-6879(06)20010-7 -
Research in Microbiology 2016
Topics: Eukaryotic Cells; Microarray Analysis; Phenotype; Prokaryotic Cells; Stress, Physiological
PubMed: 27600694
DOI: 10.1016/j.resmic.2016.08.003 -
Methods in Molecular Biology (Clifton,... 2006DNA microarray analyzes genome-wide gene expression patterns and is used in many areas including drug discovery and clinical applications. This chapter summarizes some... (Review)
Review
DNA microarray analyzes genome-wide gene expression patterns and is used in many areas including drug discovery and clinical applications. This chapter summarizes some of these applications such as identification and validation of anti-infective drug target, study mechanisms of drug action and drug metabolism, classification of different types of tumors, and use of molecular signatures for prediction of disease outcome. A step-by-step protocol is provided for sample preparation, sample labeling and purification, hybridization and washing, feature extraction, and data analysis. Important considerations for a successful experiment are also discussed with emphasis on drug discovery and clinical applications. Finally, a clinical study is presented as an example to illustrate how DNA microarray technology can be used to identify gene signatures, and to demonstrate the promise of DNA microarray as a clinical tool.
Topics: Computational Biology; Gene Expression Profiling; Humans; Microarray Analysis
PubMed: 16671400
DOI: 10.1385/1-59259-964-8:49