Interval modelling in optimization of k‐NN classifiers for large number of attributes in data sets on an example of DNA microarrays

In functional genomics, DNA microarrays for gene expression profiling are increasingly being used to provide insights into biological function or pathology. To better understand the significance of the multiple transcriptional changes across a time period, the temporal changes in phenotype must be described. Orotic acid-induced fatty liver disease was investigated at the transcriptional and metabolic levels using microarrays and metabolic profiling in two strains of rats. High-resolution 1H-NMR spectroscopic analysis of liver tissue indicated that Kyoto rats compared with Wistar rats are predisposed to the insult. Metabolite analysis and gene expression profiling following orotic acid treatment identified perturbed metabolic pathways, including those involved in fatty acid, triglyceride, and phospholipid synthesis, β-oxidation, altered nucleotide, methyl donor, and carbohydrate metabolism, and stress responses. Multivariate analysis and statistical bootstrapping were used to investigate co-responses with transcripts involved in metabolism and stress responses. This reverse functional genomic strategy highlighted the relationship between changes in the transcription of stearoyl-CoA desaturase 1 and those of other lipid-related transcripts with changes in NMR-derived lipid profiles. The results suggest that the integration of 1H-NMR and gene expression data sets represents a robust method for identifying a focused line of research in a complex system.

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Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization

Molecular Biology of the Cell ◽

10.1091/mbc.9.12.3273 ◽

1998 ◽

Vol 9 (12) ◽

pp. 3273-3297 ◽

Cited By ~ 2969

Author(s):

Paul T. Spellman ◽

Gavin Sherlock ◽

Michael Q. Zhang ◽

Vishwanath R. Iyer ◽

Kirk Anders ◽

...

Keyword(s):

Cell Cycle ◽

Cell Cycle Regulation ◽

Dna Microarrays ◽

Full Description ◽

Temperature Sensitive ◽

Data Sets ◽

Mrna Levels ◽

Yeast Cultures ◽

Yeast Genes ◽

Cycle Regulation

We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: α factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle–regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu

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Spectral Methods for Data Clustering

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch195 ◽

2011 ◽

pp. 1037-1042

Author(s):

Wenyuan Li

Keyword(s):

Data Storage ◽

Dna Microarrays ◽

Clustering Algorithms ◽

High Volume ◽

Digital Data ◽

The Internet ◽

Data Sets ◽

Time Data ◽

Low Dimensionality ◽

Tremendous Amount

With the rapid growth of the World Wide Web and the capacity of digital data storage, tremendous amount of data are generated daily from business and engineering to the Internet and science. The Internet, financial real-time data, hyperspectral imagery, and DNA microarrays are just a few of the common sources that feed torrential streams of data into scientific and business databases worldwide. Compared to statistical data sets with small size and low dimensionality, traditional clustering techniques are challenged by such unprecedented high volume, high dimensionality complex data. To meet these challenges, many new clustering algorithms have been proposed in the area of data mining (Han & Kambr, 2001).

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CD44-stimulated human B cells express transcripts specifically involved in immunomodulation and inflammation as analyzed by DNA microarrays

Blood ◽

10.1182/blood-2002-06-1837 ◽

2003 ◽

Vol 101 (6) ◽

pp. 2307-2313 ◽

Cited By ~ 16

Author(s):

Carl-Magnus Högerkorp ◽

Sven Bilke ◽

Thomas Breslin ◽

Sigurdur Ingvarsson ◽

Carl A. K. Borrebaeck

Keyword(s):

B Cells ◽

Dna Microarrays ◽

Lymphocyte Activation ◽

Cell Receptor ◽

Surface Glycoprotein ◽

Data Sets ◽

Cell Surface Glycoprotein ◽

Human B Cells ◽

Analysis Scheme

A number of studies have implicated a role for the cell surface glycoprotein CD44 in several biologic events, such as lymphopoiesis, homing, lymphocyte activation, and apoptosis. We have earlier reported that signaling via CD44 on naive B cells in addition to B-cell receptor (BCR) and CD40 engagement generated a germinal center–like phenotype. To further characterize the global role of CD44 in B differentiation, we examined the expression profile of human B cells cultured in vitro in the presence or absence of CD44 ligation, together with anti-immunoglobulin (anti-Ig) and anti-CD40 antibodies. The data sets derived from DNA microarrays were analyzed using a novel statistical analysis scheme created to retrieve the most likely expression pattern of CD44 ligation. Our results show that genes such as interleukin-6 (IL-6), IL-1α, and β2-adrenergic receptor (β2-AR) were specifically up-regulated by CD44 ligation, suggesting a novel role for CD44 in immunoregulation and inflammation.

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A parallel data clustering algorithm for Intel MIC accelerators

Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie) ◽

10.26089/nummet.v20r211 ◽

2019 ◽

pp. 104-115

Author(s):

Т.В. Речкалов ◽

М.Л. Цымблер

Keyword(s):

Dna Microarrays ◽

Clustering Algorithm ◽

Real Data ◽

Data Sets ◽

Data Layout ◽

Partitioning Around Medoids ◽

Wide Range ◽

Input Dataset ◽

Intel Mic ◽

Many Integrated Core

Алгоритм PAM (Partitioning Around Medoids) представляет собой разделительный алгоритм кластеризации, в котором в качестве центров кластеров выбираются только кластеризуемые объекты (медоиды). Кластеризация на основе техники медоидов применяется в широком спектре приложений: сегментирование медицинских и спутниковых изображений, анализ ДНК-микрочипов и текстов и др. На сегодня имеются параллельные реализации PAM для систем GPU и FPGA, но отсутствуют таковые для многоядерных ускорителей архитектуры Intel Many Integrated Core (MIC). В настоящей статье предлагается новый параллельный алгоритм кластеризации PhiPAM для ускорителей Intel MIC. Вычисления распараллеливаются с помощью технологии OpenMP. Алгоритм предполагает использование специализированной компоновки данных в памяти и техники тайлинга, позволяющих эффективно векторизовать вычисления на системах Intel MIC. Эксперименты, проведенные на реальных наборах данных, показали хорошую масштабируемость алгоритма. The PAM (Partitioning Around Medoids) is a partitioning clustering algorithm where each cluster is represented by an object from the input dataset (called a medoid). The medoid-based clustering is used in a wide range of applications: the segmentation of medical and satellite images, the analysis of DNA microarrays and texts, etc. Currently, there are parallel implementations of PAM for GPU and FPGA systems, but not for Intel Many Integrated Core (MIC) accelerators. In this paper, we propose a novel parallel PhiPAM clustering algorithm for Intel MIC systems. Computations are parallelized by the OpenMP technology. The algorithm exploits a sophisticated memory data layout and loop tiling technique, which allows one to efficiently vectorize computations with Intel MIC. Experiments performed on real data sets show a good scalability of the algorithm.

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Formation and Composition of the Bacillus anthracis Endospore

Journal of Bacteriology ◽

10.1128/jb.186.1.164-178.2004 ◽

2004 ◽

Vol 186 (1) ◽

pp. 164-178 ◽

Cited By ~ 150

Author(s):

Hongbin Liu ◽

Nicholas H. Bergman ◽

Brendan Thomason ◽

Shamira Shallom ◽

Alyson Hazen ◽

...

Keyword(s):

Gene Expression ◽

Bacillus Anthracis ◽

Dna Microarrays ◽

Data Sets ◽

Dependent Manner ◽

Anthrax Spores ◽

Complementary Nature ◽

And Function ◽

Protective Proteins ◽

Physical Construction

ABSTRACT The endospores of Bacillus anthracis are the infectious particles of anthrax. Spores are dormant bacterial morphotypes able to withstand harsh environments for decades, which contributes to their ability to be formulated and dispersed as a biological weapon. We monitored gene expression in B. anthracis during growth and sporulation using full genome DNA microarrays and matched the results against a comprehensive analysis of the mature anthrax spore proteome. A large portion (∼36%) of the B. anthracis genome is regulated in a growth phase-dependent manner, and this regulation is marked by five distinct waves of gene expression as cells proceed from exponential growth through sporulation. The identities of more than 750 proteins present in the spore were determined by multidimensional chromatography and tandem mass spectrometry. Comparison of data sets revealed that while the genes responsible for assembly and maturation of the spore are tightly regulated in discrete stages, many of the components ultimately found in the spore are expressed throughout and even before sporulation, suggesting that gene expression during sporulation may be mainly related to the physical construction of the spore, rather than synthesis of eventual spore content. The spore also contains an assortment of specialized, but not obviously related, metabolic and protective proteins. These findings contribute to our understanding of spore formation and function and will be useful in the detection, prevention, and early treatment of anthrax. This study also highlights the complementary nature of genomic and proteomic analyses and the benefits of combining these approaches in a single study.

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Understanding DNA Microarrays: Sources and Magnitudes of Variances in DNA Microarray Data Sets

Genomics, Proteomics and Vaccines ◽

10.1002/0470012536.ch4 ◽

2005 ◽

pp. 75-101

Author(s):

She-Pin Hung ◽

Suman Sundaresh ◽

Pierre F. Baldi ◽

G. Wesley Hatfield

Keyword(s):

Dna Microarray ◽

Microarray Data ◽

Dna Microarrays ◽

Data Sets ◽

Dna Microarray Data

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Genomic approaches to hematologic malignancies

Blood ◽

10.1182/blood-2004-01-0274 ◽

2004 ◽

Vol 104 (4) ◽

pp. 923-932 ◽

Cited By ~ 88

Author(s):

Benjamin L. Ebert ◽

Todd R. Golub

Keyword(s):

Gene Expression ◽

Dna Microarrays ◽

Molecular Taxonomy ◽

Large Data ◽

Hematologic Malignancies ◽

Future Research ◽

Data Sets ◽

Statistical Interpretation ◽

Molecular Abnormalities ◽

Molecular Profiles

AbstractIn the past several years, experiments using DNA microarrays have contributed to an increasingly refined molecular taxonomy of hematologic malignancies. In addition to the characterization of molecular profiles for known diagnostic classifications, studies have defined patterns of gene expression corresponding to specific molecular abnormalities, oncologic phenotypes, and clinical outcomes. Furthermore, novel subclasses with distinct molecular profiles and clinical behaviors have been identified. In some cases, specific cellular pathways have been highlighted that can be therapeutically targeted. The findings of microarray studies are beginning to enter clinical practice as novel diagnostic tests, and clinical trials are ongoing in which therapeutic agents are being used to target pathways that were identified by gene expression profiling. While the technology of DNA microarrays is becoming well established, genome-wide surveys of gene expression generate large data sets that can easily lead to spurious conclusions. Many challenges remain in the statistical interpretation of gene expression data and the biologic validation of findings. As data accumulate and analyses become more sophisticated, genomic technologies offer the potential to generate increasingly sophisticated insights into the complex molecular circuitry of hematologic malignancies. This review summarizes the current state of discovery and addresses key areas for future research.

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129930 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1060-1061

Author(s):

Mark Ellisman ◽

Maryann Martone ◽

Gabriel Soto ◽

Eleizer Masliah ◽

David Hessler ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Three Dimensional ◽

Neuritic Plaque ◽

Dimensional Structure ◽

Data Sets ◽

Molecular Physiology ◽

Research Activities ◽

Computer Aided ◽

Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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