Wyniki wyszukiwania - BazTech

1

Analysis of entity-attribute-value model applications in freely available database management systems for dna microarray data processing

Waller T., Zapart D., Tkacz M., Wróbel Z.

Journal of Medical Informatics & Technologies

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2012

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Vol. 20

59--63

EN

Large volumes of data are generated during DNA microarrays experiments. Database management systems (DBMS) are increasingly applied to these data, providing optimum processing and management from multiple microarray experiments. In this study, freely accessible DBMS software versions were compared (Microsoft SQL Server 2008 Express Edition, Oracle Database 10g Express Edition, DB2 Express-C 9.7.2, MySQL 5.1, and PostgreSQL 9.0). We examined them in the context of possible Entity-Attribute-Value (EAV) application as an optimal organization method for microarray data. It was confirmed in the comparative analysis of component data processing methods, consistent with the EAV model, that efficient methods for microarray data analysis are available in Microsoft SQL Server 2008 Express Edition and PostgreSQL 9.0 systems. Also, DNA microarray data processing was confirmed to be more efficient with Microsoft SQL Server 2008 Express Edition as compared with PostgreSQL 9.0. The EAV method was also shown to be suitable for use with open-source versions of DBMS software as an optimum storage model for DNA microarray data. In terms of data processing methods and performance, the Microsoft SQL Server 2008 Express Edition proved to be the best among compared database systems.

2

Analysis of GO composition of gene clusters by using multiattribute decision rules

Gruca A.

Biocybernetics and Biomedical Engineering

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2008

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Vol. 28, no. 4

21-32

EN

In this paper, a novel method for characterizing the Gene Ontology (GO) composition of the gene clusters on basis of the decision rules is presented. The rules are expressed as logical functions of the Gene Ontology terms which are interpreted as binary attributes. A new method for evaluating the quality of decision rules based on statistical significance is developed. The presented approach is applied to the well-known data set and the results are compared with the results obtained by other authors.

3

Performance of classification methods in a microarray setting: a simulation study

Van Sanden S., Lin D., Burzykowski T.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 3

15-28

EN

Dudoit et al., Lee et al., and Statnikov et al. investigated the performance of several classification methods applied to real-life microarray data. Due to the availability of only a few datasets, only a limited number of settings could be evaluated. Also, the true classification and the set of truly differentially expressed genes were unknown. In order to overcome these limitations, a simulation study was conducted, by using a linear mixed effects model to simulate microarray data under different scenarios. Several classification methods were compared with respect to their ability to discriminate between two classes of biological samples in various experimental settings.

4

Cellular neural network application to cDNA microarray image analysis

Kaniewski L., Kacprzak T.

Przegląd Elektrotechniczny

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2006

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R. 82, nr 1

74-79

EN

Huge amount of data presented in a single cDNA microarray is a challenge for contemporary data analysis systems due to its time consuming processing. We present an extension of new approach to the cDNA microarray image analysis in real time by means of Cellular Neural Networks (CNN), which can perform its function using locally connected elemental analogue processing units organized in rectangle array corresponding to the cDNA array. Based on this approach we expect to formulate fundamental requirements for VLSI chip implementation to realize a gene expression profile of given cDNA array in a real time.

PL

Olbrzymia ilość danych zawartych w pojedynczej mikromacierzy cDNA jest dużym wyzwaniem dla współczesnych systemów przetwarzania, głównie z powodu konieczności wykonywania czasochłonnych obliczeń. W referacie przedstawiono rozwinięcie koncepcji zastosowania do tych celów sieci neuronowej komórkowej, która wykonuje funkcje przetwarzania w oparciu o architekturę prostokątną podstawowych jednostek analogowych połączonych ze sobą lokalnie i odpowiadającą mikromacierzy cDNA. W oparciu o wyniki symulacji należy oczekiwać opracowania podstawowych wymagań projektu układu scalonego VLSI, który mógłby wykonywać zadanie zbadania poziomu ekspresji genów w czasie rzeczywistym.

5

Analiza danych z mikromacierzy DNA metody, narzędzia

Maciejewski H, Konarski Ł., Jasińska A., Drath M.

Bio-Algorithms and Med-Systems

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2005

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Vol. 1, no. 1/2

129--132

PL

Praca poświęcona jest analizie danych z badań ekspresji genów prowadzonych za pomocą mikromacierzy DNA. Mikromacierze DNA są nową techniką wykorzystywaną przez biologów, a coraz powszechniej również przez lekarzy, umożliwiającą równoczesny pomiar poziomu ekspresji wielu (np. kilkudziesięciu tysięcy) genów badanego organizmu. Eksperymenty za pomocą mikromacierzy prowadzą do powstawania dużych zbiorów danych, na podstawie których możliwe jest poszukiwanie zależności pomiędzy aktywnością genów a np. stanami chorobowymi organizmu, badanie funkcji genów, a także budowanie modeli umożliwiających przewidywanie reakcji na terapie i in. Poszukiwanie tego typu zależności, istotnych dla biologów i lekarzy, możliwe jest dzięki zastosowaniu zaawansowanych metod data mining, korzystających zarówno z technik statystycznych, jak i rozpoznawania wzorców (pattern recognition),a także specjalizowanych narzędzi komputerowych. Specyfika danych mikromacierzowych oraz formułowanych dla nich zadań analitycznych dała nowe impulsy rozwoju metod analizy danych oraz narzędzi komputerowych. W pracy przedstawiono zadania analizy danych formułowane dla potrzeb analizy danych z badań mikromacierzowych. Szczególną uwagę zwrócono na zadania grupowania w celu poszukiwania grup genów przejawiających podobne profile ekspresji oraz grup podobnych obserwacji (próbek). Pokazano wpływ ustawień zastosowanych algorytmów analitycznych na wyniki, w ten sposób ilustrując trudności interpretacji wyników analizy danych z badań mikromacierzowych. W pracy omówiono również wybrane narzędzia wspomagające analizę danych mikromacierzowych, a także rozwijane ostatnio narzędzia umożliwiające efektywną pracę interdyscyplinarnych zespołów badaczy biologów - statystyków - informatyków, pracujących nad analizą danych z mikromacierzy DNA.

EN

The purpose of the work is to present application of data mining techniques for analysis of results from DNA microarray experiments. A challenging problem of finding groups of genes with similar expression patterns and groups of samples with similarly expressed genes is discussed in greater detail. Software tools that expedite data analysis by building a frame-work for efficient cooperation of interdisciplinary teams consisting of biologists, statisticians, and data mining specialists are also presented.

6

The analysis of chromatin condensation state and transcriptional activity using DNA microarrays

Widłak P., Fujarewicz K.

Journal of Medical Informatics & Technologies

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2003

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Vol. 6

IP13--19

EN

The DNA microarray-based technique has been developed to semi-quantitatively measure the in vivo global chromatin condensation state at the resolution of a gene. Chromatin was fractionated due to the differential solubility of histone H1-containing and histone H1-free nucleosomes. A set of genes non-randomly distributed between histone H1-free (uncondensed or open) and histone H1-containing (condensed or closed) chromatin fractions has been identified. The transcript levels have been measured for the same group of genes. The correlation between transcriptional activity and chromatin fraction distribution of particular genes has been established.

7

Application of the Adaptive Center-Weighted Vector Median Framework for the Enhancement of cdna Microarray Images

Lukac R., Smołka B.

International Journal of Applied Mathematics and Computer Science

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2003

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Vol. 13, no 3

369-383

EN

In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of the so called gene chips. We demonstrate that the new technique is capable of reducing the impulsive noise present in microarray images and that it facilitates efficient spot location and the estimation of the gene expression levels due to the smoothing effect and preservation of the spot edges. This paper contains a comparison of the new technique of impulsive noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.

8

On the noise attenuation in DNA microarray images

Smolka B., Szczepański M., Wojciechowski K., Plataniotis K. N.

Journal of Medical Informatics & Technologies

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2001

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Vol. 2, Part. 1

MI19--MI27

EN

In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of gene chips. We demonstrate that the new technique is capable of reducing various kinds of noise present in microarray images and that it enables efficient spot location and estimation of the gene expression level due to the smoothing effect and preservation of the spot edges. This paper contains the comparison of the new technique of noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.