Wyniki wyszukiwania - BazTech

1

A robust pre-processing of BeadChip microarray images

Kalina J.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

556--563

EN

Microarray images commonly used in gene expression studies are heavily contaminated by noise and/or outlying values (outliers). Unfortunately, standard methodology for the analysis of Illumina BeadChip microarray images turns out to be too vulnerable to data contamination by outliers. In this paper, an alternative approach to low-level pre-processing of images obtained by the BeadChip microarray technology is proposed. The novel approach robustifies the standard methodology in a complex way and thus ensures a sufficient robustness (resistance) to outliers. A gene expression data set from a cardiovascular genetic study is analyzed and the performance of the novel robust approach is compared with the standard methodology. The robust approach is able to detect and delete a larger percentage of outliers. More importantly, gene expressions are estimated more precisely. As a consequence, also the performance of a subsequently performed classification task to two groups (patients vs. control persons) is improved over the cardiovascular gene expression data set. A further improvement was obtained when considering weighted gene expression values, where the weights correspond to a robust estimate of variability of the measurements for each individual gene transcript.

2

MicroRNA expression prediction: Regression from regulatory elements

Oğul H., Tuncer M. E.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

89--94

EN

MicroRNAs are known as important actors in post-transcriptional regulation and relevant biological processes. Their expression levels do not only provide information about their own activities but also implicitly explain the behaviors of their targets, thus, in turn, the circuitry of underlying gene regulatory network. In this study, we consider the problem of estimating the expression of a newly discovered microRNA with known promoter sequence in a certain condition where the expression values of some known microRNAs are available. To this end, we offer a regression model to be learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from the regulatory elements found in its promoter sequence. The results obtained through the experiments on real microarray data justify the applicability of the framework in practice.

3

Feature selection methods in application to gene expression: autism data

Latkowski T, Osowski S.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 8

199-204

EN

The paper presents the application of several different feature selection methods for recognizing the most significant genes and gene sequences (treated as features) stored in dataset of gene expression microarray related to autism. The outcomes of each method have been examined by analyzing gene expression profiles of selected genes. In the next step fusion of the most relevant features selected by different methods, has been implemented. The optimal number of features has been defined as the set providing the best clustering purity.

PL

Praca prezentuje badanie wybranych metod selekcji cech diagnostycznych w celu wyodrębnienia najbardziej znaczących sekwencji genowych z mikromacierzy ekspresji genów dotyczącej autyzmu. Dla wyselekcjonowanych cech przeanalizowano wartości poziomów ekspresji genów. W kolejnym etapie dokonano fuzji wyselekcjonowanych cech. Optymalny zbiór cech wyznaczono na podstawie czystości przestrzeni klasteryzacji.

4

Decision tree approach to microarray data analysis

Grześ M., Krętowski M.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 3

29-42

EN

The classification of gene expression data is still new, difficult and also an interesting field of endeavour. There is a demand for powerful approaches to this problem, which is one of the ultimate goals of modern biological research. Two different techniques for inducing decision trees are discussed and evaluated on well-known and publicly available gene expression datasets. Empirical results are presented.

5

Methods of microarray data preprocessing for classification of tumors

Rubel T., Pawłowski Z., Zaremba K.

Biocybernetics and Biomedical Engineering

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2005

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Vol. 25, no. 4

37-57

EN

Gene arrays measure expression levels for thousands of genes simultaneously, providing a powerful tool for both basic research and clinical medicine. The aim of this paper was to present an optimal approach to preprocessing data from cancer microarray studies. The performance of different statistical methods used for the tumor classification was also compared. These methods include: the Bayes classifier, Fisher's classifier, minimum Euclidean and Mahalanobis distance classifiers and K-nearest neighbours classifier. The preprocessing algorithms and classification methods were applied to three datasets used for diagnosis of lymphoma, leukemia and lung cancer.

6

BioChip - narzędzie do analizy DNA

Ilnicki F.

Przegląd Elektrotechniczny

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2004

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R. 80, nr 4

277-282

PL

W artykule został zaprezentowany BioChip (jeden z rodzajów biosensora) oraz jego zastosowanie do badań DNA. Pierwsza część zawiera niezbędne podstawowe informacje, tzn. podstawy biologiczne, definicje użytych pojęć oraz istotę zastosowania. W dalszej części zostało przedstawione porównanie obecnie stosowanych BioChipów, pod kątem technologii i użytkowania.

EN

The paper introduce BioChip (type of Biosensor), and it's usage to DNA tests. First part contains basic information i. e. required biological base, used definitions and essence of application of BioChips. Last part contains comparison of currently used BioChips from the technological and operational point of view.