Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Journal of Medical Informatics & Technologies

2 2011

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: klasyfikator najbliższych sąsiadów

Sortuj według:

Ogranicz wyniki do:

Estimation of significance of AlkB and AlkA proteins in DNA repair in Escherichia coli model

Sokołowska B., Maciejewska A. M., Jóźwik A., Kuśmierek J. T.

Journal of Medical Informatics & Technologies

2011

Vol. 17

321--326

The paper concerns estimation of significance of differences of mutagenesis level between the wild-type strain (wt) and its derivatives which differ in DNA repair ability, namely alkA and alkB strain, devoided AlkA glycosylase and AlkB dioxygenase activity, respectively. The strains were analyzed for their ability to repair 1,N6-ethenoadenine (εA) - chloroacetaldehyde adduct to DNA. The analysis was done using classical statistical and pattern recognition methods. The obtained results confirmed that AlkB dioxygenase plays the most important role in εA repair in E. coli in the experimental modeling.

An approach to unsupervised classification

Przybyła T., Pander T., Horoba K., Kupka T., Matonia A.

Journal of Medical Informatics & Technologies

2011

Vol. 17

105--111

Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.