The lack of efficient techniques for assessing the biological implications of microarray gene-expression data remains an important obstacle in exploiting this information. To address this need, a mining technique has been developed based on the analysis of literature profiles generated by extracting the frequencies of certain terms from thousands of abstracts stored in the Medline literature database.

The dataset is transformed into Matlab format. They are designed to be in cell formats. Each cell is a matrix which consists of a column representing the gene and row for the subject.Each dataset is organized in a separate directory. The directory contains four versions: a) Original dataset, b) Imputed dataset by MEAN,c) Imputed dataset by MEDIAN,d) Imputed dataset by Most Frequent,

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.

This dataset tracks the updates made on the dataset "Mining microarray expression data by literature profiling" as a repository for previous versions of the data and metadata.

Microarrays are at the center of a revolution in biotechnology, allowing researchers to screen tens of thousands of genes simultaneously. Typically, they have been used in exploratory research to help formulate hypotheses. In most cases, this phase is followed by a more focused, hypothesis driven stage in which certain specific biological processes and pathways are thought to be involved. Since a single biological process can still involve hundreds of genes, microarrays are still the preferred approach as proven by the availability of focused arrays from several manufacturers. Since focused arrays from different manufacturers use different sets of genes, each array will represent any given regulatory pathway to a different extent. We argue that a functional analysis of the arrays available should be the most important criterion used in the array selection. We developed Onto-Compare as a database that can provide this functionality, based on the GO nomenclature. Compare commercially available microarrays based on GO. User account required. Platform: Online tool

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).

Pairwise alignment approaches for time-varying gene expression profiles have been recently developed for the detection of co-expressions in time-series microarray data sets. In this paper, we analyze multiple expression profile alignment (MEPA) methods for classifying microarray time-course data. We apply a nearest centroid classification technique, in which the centroid of each class is computed by means of a MEPA algorithm. MEPA aligns the expression profiles in such a way to minimize the total area between all aligned profiles. We propose four MEPA approaches whose effectiveness are demonstrated on the well-known budding yeast, S. cerevisiae, data set. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

We present a quantitative analysis of small RNA dynamics during the transition from hPSCs to the three germ layer lineages to identify spatiotemporal-specific small RNAs that may be involved in hPSC differentiation. To determine the degree of spatiotemporal specificity, we utilized two algorithms, namely normalized maximum timepoint specificity index (NMTSI) and across-tissue specificity index (ASI). NMTSI could identify spatiotemporal-specific small RNAs that go up or down at just one timepoint in a specific lineage. ASI could identify spatiotemporal-specific small RNAs that maintain high expression from intermediate timepoints to the terminal timepoint in a specific lineage. Beyond analyzing single small RNAs, we also quantified the spatiotemporal-specificity of microRNA families and observed their differential expression patterns in certain lineages. To clarify the regulatory effects of group miRNAs on cellular events during lineage differentiation, we performed a gene ontology (GO) analysis on the downstream targets of synergistically up- and downregulated microRNAs. To provide an integrated interface for researchers to access and browse our analysis results, we designed a web-based tool at https://keyminer.pythonanywhere.com/km/.

Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that "significant" features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

The diamagnetic levitation as a novel ground-based model for simulating a reduced gravity environment has recently been applied in life science research. In this study a specially designed superconducting magnet with a large gradient high magnetic field (LG-HMF), which can provide three apparent gravity levels (μ-g, 1-g, and 2-g), was used to simulate a space-like gravity environment. Osteocyte, as the most important mechanosensor in bone, takes a pivotal position in mediating the mechano-induced bone remodeling. In this study, the effects of LG-HMF on gene expression profiling of osteocyte-like cell line MLO-Y4 were investigated by Affymetrix DNA microarray. LG-HMF affected osteocyte gene expression profiling. Differentially expressed genes (DEGs) and data mining were further analyzed by using bioinfomatic tools, such as DAVID, iReport. 12 energy metabolism related genes (PFKL, AK4, ALDOC, COX7A1, STC1, ADM, CA9, CA12, P4HA1, APLN, GPR35 and GPR84) were further confirmed by real-time PCR. An integrated gene interaction network of 12 DEGs was constructed. Bio-data mining showed that genes involved in glucose metabolic process and apoptosis changed notablly. Our results demostrated that LG-HMF affected the expression of energy metabolism related genes in osteocyte. The identification of sensitive genes to special environments may provide some potential targets for preventing and treating bone loss or osteoporosis.

NASCArrays is the Nottingham Arabidopsis Stock Centre''s microarray database. Currently most of the data is for Arabidopsis thaliana experiments run by the NASC Affymetrix Facility. There are also experiments from other species, and experiments run by other centres too. NASCArrays is an Affymetrix microarray database. It contains free Affymetrix microarray data, and also features a series of tools allowing you to query that data in powerful ways. Most of the data currently comes from NASC''s Affymetrix Service. It also includes data from other sources, notably the AtGenExpress project. They currently distribute over 30,000 tubes of seed a year. There are currently the following data mining tools available. All of these tools allow you to type in a gene(s) of interest, and identify experiments or slides that you might be interested in: -Spot History: This tool allows you to see the pattern of gene expression over all slides in the database. Easily identify slides (and therefore experimental treatments) where genes are highly, lowly, or unusually expressed -Two gene scatter plot: This tool allows you to see the pattern of gene expression over all slides for two genes as a scatter plot. If you are interested in two genes, you can find out if they act in tandem, and highlight slides (and therefore experimental conditions) where these two genes behave in an unusual manner. -Gene Swinger: If you have a gene of interest, this tool will show you which experiment the gene expression varied most -Bulk Gene Download: This tool allows you to download the expression of a list of genes over all experiments. You can get all genes over all experiments (the entire database!) from the Super Bulk Gene Download Sponsors: This is a BBSRC funded consortium to provide services to the Arabidopsis community.

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Microarray Biochips Market Size 2024-2028

The microarray biochips market size is forecast to increase by USD 17.28 billion, at a CAGR of 22.2% between 2023 and 2028.

The market is characterized by a growing number of collaborations among key players, which is expanding market presence and driving innovation. This strategic approach is essential in the capital-intensive market, where significant investments are required for research and development. A notable trend in the market is the emergence of Label-One-Component (LOC) technology, offering advantages such as improved sensitivity and specificity. However, the high cost of microarray biochips remains a significant challenge for market growth. Companies seeking to capitalize on opportunities must navigate this obstacle by focusing on cost reduction through economies of scale and process optimization. Additionally, collaborations and partnerships can help share research and development costs and accelerate time-to-market for innovative products. The strategic landscape of the market is dynamic, with ongoing advancements in technology and a growing demand for personalized medicine, creating opportunities for companies to differentiate themselves and gain a competitive edge.

What will be the Size of the Microarray Biochips Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in technology and expanding applications across various sectors. Protein microarray technology, a crucial component, enables high-throughput analysis of protein-protein interactions and antibody discovery. Reproducibility metrics and spot morphology analysis ensure consistency and accuracy in data generation. Label incorporation methods, such as biotinylated target cDNA and reverse transcription PCR, facilitate efficient probe attachment. Gene ontology enrichment and pathway analysis tools provide insights into biological functions and molecular interactions. Data mining algorithms, including clustering algorithms and fold change calculations, facilitate pattern recognition and discovery. Microarray data normalization techniques, such as CDNA microarray platforms and genomic DNA extraction, ensure data consistency. Microarray experimental design, hybridization kinetics, and high-throughput screening are essential for optimizing data generation and analysis. Single nucleotide polymorphism (SNP) detection and comparative genomic hybridization offer valuable insights into genetic variations. Data quality assessment, signal-to-noise ratios, and background correction methods ensure data accuracy and reliability. In situ hybridization and fluorescence detection methods facilitate visualization and analysis of gene expression at the cellular level. Differential gene expression analysis provides insights into disease mechanisms and therapeutic targets. Microarray scanner systems and image analysis software facilitate efficient and accurate data analysis. DNA microarray technology continues to evolve, offering exciting possibilities for research and diagnostic applications.

How is this Microarray Biochips Industry segmented?

The microarray biochips industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ApplicationDrug discovery and developmentDiagnostics and treatmentsResearch and consumablesForensic medicinesOthersGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Application Insights

The drug discovery and development segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth due to its increasing application in drug discovery, driven by the rising preference for personalized medicines. With the global population aging, the demand for better healthcare solutions is escalating, leading manufacturers to continually innovate and improve microarray technology. In genomics and proteomics, microarray biochips are increasingly utilized, further fueling market growth. Advancements in protein microarray technology ensure greater reproducibility and accuracy, while spot morphology analysis and label incorporation enhance data reliability. Gene ontology enrichment and pathway analysis tools enable deeper insights into biological processes, and clustering algorithms facilitate the identification of complex relationships between genes. Genomic DNA extraction and microarray data normalization are crucial steps in ensuring data quality, while high-throughput screening and single nucleotide polymorphism analysis accelerate research. Image analysis software, biotinylated target cDNA, reverse transcription PCR, and

DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.

A microarray-experiment oriented warehouse for collections of expression data, integrated with gene annotation profiling and used to support genomic data mining processes. It provides means to access and extract valuable information from a Laboratory Information Management System (LIMS) and makes use of several plug-ins to process and analyze the data. The system consists of two parts: MIAME compliant data storage is handled by the LIMS while data analysis is performed in the DWH. The core of the system is BASE. Accessing LIMS and DWH is accomplished through secure connections.

Effective reproduction is essential for the survival and proliferation of any organism, from the birth of new offspring to the repro- duction of individual cells. Each portion of a cell’s DNA must be copied exactly once during the replication phase of its cell cycle to ensure viability. In humans, this is achieved by a complex pattern of replication origins and terminations along the chromosomes until the final product is realized. DNA Tiling Microarrays are utilized to assay discrete pools of DNA replicated during different parts of the replication phase. We present a generalized framework for analyzing this discrete timing data to recover a relatively continuous profile of the DNA replication timing. This approach can be used to assay DNA replication timing over a variety of human cell lines or extended to other organisms. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

THIS RESOURCE IS NO LONGER IN SERVICE; REPLACED BY NEPHROSEQ; A growing database of publicly available renal gene expression profiles, a sophisticated analysis engine, and a powerful web application designed for data mining and visualization of gene expression. It provides unique access to datasets from the Personalized Molecular Nephrology Research Laboratory incorporating clinical data which is often difficult to collect from public sources and mouse data.