Wyniki wyszukiwania - BazTech

1

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.

2

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.

3

Learning Rough Set Classifiers from Gene Expressions and Clinical Data

Midelfart H., Komorowski J., Norsett K., Yadetie F., Sandvik A.K., Laegreid A.

Fundamenta Informaticae

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2002

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Vol. 53, nr 2

155-183

EN

Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance. One application of microarray technology is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We present a general Rough Set approach for classification of tumor samples analyzed with microarrays. This approach is tested on a data set of gastric tumors, and we develop classifiers for six clinical parameters. One major obstacle in training classifiers from microarray data is that the number of objects is much smaller that the number of attributes. We therefore introduce a feature selection method based on bootstrapping for selecting genes that discriminate significantly between the classes, and study the performance of this method. Moreover, the efficacy of several learning and discretization methods implemented in the ROSETTA system [18] is examined. Their performance is compared to that of linear and quadratic discrimination analysis. The classifiers are also biologically validated. One of the best classifiers is selected for each clinical parameter, and the connection between the genes used in these classifiers and the parameters are compared to the establish knowledge in the biomedical literature.