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1

Application of Density Based Clustering to Microarray Data Analysis

Raczynski L., Wozniak K., Rubel T., Zaremba K.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 3

281-286

EN

In just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.

2

Proteins and peptides identification from MS/MS data in proteomics

Raczyński L., Rubel T., Zaremba K.

Biocybernetics and Biomedical Engineering

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2010

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Vol. 30, no. 3

35-47

EN

Protein identification in biological samples is the most important task in proteomics. In the past decade, mass spectrometry (MS) became the method of choice for the identification of proteins. The purpose of this paper is to give an overview of MS-based protein identification methods, discuss their advantages and limitations and to highlight some recent advancements in this field.

3

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.