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Genetic Engineering
Published in Shintaro Furusaki, John Garside, L.S. Fan, The Expanding World of Chemical Engineering, 2019
Up to now, two types of microarray have been developed. One is a micro array of cDNA. This type of microarray is prepared by spotting DNA solution onto a slide glass or niron membrane. Due to the technical improvements in the spotting machine, the DNA solution can be spotted reproducibly with a spot diameter of 200 µm and the distance of each spot is 300 µm. After hybridization, hybridized spots can be detected with a laser scanner if hybridized messenger RNA is labeled with fluorescent dye. By using this type of microarray, the expression level of genes can be assessed very easily. For instance, gene expression profiles in cancer progression are now under investigation for about 9 typical cancers which may give important information on the mechanism of carcinogenesis and cancer diagnosis. The human genome contains hundreds of thousands of genes. Therefore, once a complete collection of human cDNA is established, a wide variety of applications will be possible.
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Published in P. Dakin John, G. W. Brown Robert, Handbook of Optoelectronics, 2017
Constantinos Pitris, Tuan Vo-Dinh, R. Eugene Goodson, Susie E. Goodson
The most noteworthy impact of microarray and biochip technologies, in conjunction with bioinformatics, is the facilitation of an entirely new approach to biological and biomedical research. In the past, researchers investigated one or a few genes at a time. With new, automated, high-throughput, microarray, and biochip technologies, they can now study a medical problem systematically and on a large scale. They can examine all the genes in a genome or all the gene products in a particular tissue, tumor, or organ in the context of the interconnected pathways of a living system. Such knowledge will have a profound impact on the manner by which disorders are diagnosed, treated, and/or prevented and can bring about revolutionary changes in biomedical research and clinical practice [7]. Looking further into the future, the ultimate challenge in biochip research is to realize a truly implantable sensor for reliable, real-time, in vivo health monitoring. In order to reach this goal, issues of biocompatibility, remote detection, wireless telemetry, and miniaturization will have to be successfully addressed [7].
Biomems
Published in Simona Badilescu, Muthukumaran Packirisamy, BioMEMS, 2016
Simona Badilescu, Muthukumaran Packirisamy
Nucleic acids, DNA, and RNA are molecules holding the genetic information in all life cells, and also are the fundamental components of genes, monitoring the functioning of living organisms. DNA chips are considered new and powerful tools that could combine the integration ability of microelectronic devices for simultaneous analysis of thousands of nucleic acids. In the coming years, DNA microarray technology will substantially support molecular biological research. The main challenge of the postgenomic era is to use genomic structural information to display and analyze biological processes on a genome-wide scale, and to assign the gene function. The availability of the complete human genome and of several other organisms allows the application of microarray technologies to several model organisms. Microarray technology has evolved from the old method of Southern blotting, where fragmented DNA is attached to a substrate and then probed with a known gene or fragment. Early gene arrays were made by spotting cDNA onto filter paper with a pin-spotting device. The use of microarrays for gene expression profiling was first reported in 1995.2 The nucleic acid microarray may use short oligonucleotides (15 to 25 nt), long oligonucleotides (50 to 120 nt), or polymerase chain reaction (PCR)-amplified cDNAs as array elements (the unit nt indicates nucleotides). The array elements are derived from individual genes located at defined positions on a solid support, enabling the analysis of thousand of genes in parallel, by specific hybridization.
A gravity inspired clustering algorithm for gene selection from high-dimensional microarray data
Published in The Imaging Science Journal, 2023
P. Jayashree, V. Brindha, P. Karthik
Microarray technology is a technology enabling the measurement of levels of expression of various genes in a sample. A microarray is an array of a large number of spots of DNA on a substrate, which, on interaction with a sample, gives us a way to quantify the expression levels of each gene. It is basically a Lab on a Chip, and usually has a glass slide, or thin silicon film cell for the substrate. Each spot on the microarray is made up of several identical molecules of a known DNA sequence and is known as a probe or reporter. Each spot typically consists of a few pico moles (10–12 moles) of identical DNA molecules. The principle behind the working of a microarray is that nuclei strands hybridize with strands containing complementary nucleotide units. Each spot thus measures the expression level of the DNA sequence complementary to itself Figure 1.
Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning
Published in Cybernetics and Systems, 2023
Çiğdem Erol, Tchare Adnaane Bawa, Yalçın Özkan
DNA microarray analysis is one of the technologies that help to measure the expression levels of multiple genes simultaneously via chips. It is possible to define the gene expression profile of the tumor with DNA microarray technology (D'Angelo, Di Rienzo, and Ojetti 2014). Gene expression analysis is a study used to classify cancers, predict clinical outcomes, and discover disease-associated biomarkers (Chen et al. 2014). In general, microarray studies require experimental intensive labor, time, and cost. The data obtained as a result of all these efforts are shared in public databases such as the National Center for Biotechnology Information, Gene Expression Omnibus (NCBI GEO), as well as being used in the publication in which it was produced. In order to obtain valuable information from data in today’s data age, it is necessary to conduct research in these data stacks with different perspectives, new algorithms, and new approaches.
A New Hybrid Cuckoo Search Algorithm for Biclustering of Microarray Gene-Expression Data
Published in Applied Artificial Intelligence, 2018
R. Balamurugan, A.M. Natarajan, K. Premalatha
The DNA microarray analysis is a technology which enables the researchers to analyze the expression level of thousands of genes in a single reaction rapidly and in an efficient manner (Lockhart and Winzeler 2000). A typical DNA microarray analysis involves a multistep procedure which includes fabrication of microarrays by fixing properly designed oligonucleotides representing specific genes, hybridization of complementary DNA (cDNA) populations onto the microarray, scanning hybridization signals, image analysis and normalization of data. After a number of preprocessing steps, the low-level microarray analysis of a microarray can be represented as a numerical matrix. In this matrix, the rows represent different genes and columns represent experimental conditions. Each element of this matrix represents the expression level of a gene under a specific condition, and is represented by a real number. In gene-expression matrix, a common goal is to group the genes and conditions into subsets that convey biological significance. In its most common form, this task translates to the computational problem known as clustering.