Wyniki wyszukiwania - BazTech

1

Methods of normalization the results of gene ontology term similarity

Stypka Ł., Kozielski M.

Studia Informatica

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2014

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Vol. 35, nr 2

7--18

EN

The article addresses the issue of improvement of the results quality when Gene Ontology (GO) term similarity is calculated. Several GO similarity measures produce results out of the range [0; 1]. Whereas, in order to compare different similarity measures or apply further processing, it is needed to normalise the results to this range. The most popular and well-known method of normalization is the min-max normalization. The article introduces seven normalization functions of different characteristics that can improve the results of the analysis. The comparison of the analysed methods on three different gene datasets and their evaluation is presented in this paper.

PL

Artykuł porusza problem normalizacji podobieństwa wyznaczonego dla terminów ontologii Gene Ontology (GO). Wiele metod pozwalających wyznaczyć podobieństwo terminów GO daje wyniki spoza przedziału [0; 1], podczas gdy przedział ten jest wymagany w celu porównania wybranych metod oraz dalszych analiz. W niniejszej pracy zaprezentowano siedem różnych funkcji normalizacyjnych oraz ich porównanie w odniesieniu do metody normalizacji min-max. Badania zostały przeprowadzone na trzech zbiorach genów o różnej charakterystyce.

2

Gene Ontology based gene analysis in graph database environment

Kozielski M., Stypka Ł.

Studia Informatica

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2013

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Vol. 34, nr 2A

325--336

EN

The article presents evaluation of the application of Neo4j graph database to Gene Ontology graph analysis. Graph-based term similarity measures are calculated in order to assess the effectiveness of the system. Two types of common ancestor search are presented and evaluated, and parallel execution of the analysis is also evaluated.

PL

Artykuł przedstawia ocenę zastosowania grafowej bazy danych Neo4j do analizy grafu ontologii Gene Ontology. Ocena systemu została przeprowadzona na podstawie obliczenia bazujących na analizie grafu miar podobieństwa terminów ontologii. Przedstawione i ocenione zostały dwa sposoby wyszukiwania rodziców w grafie. Analizie poddano również równoległą realizację badanych algorytmów.

3

Rule based functional description of genes – Estimation of the multicriteria rule interestingness measure by the UTA method

Gruca A., Sikora M.

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 4

222--234

EN

In this paper we present new extension of RuleGO rule generation method. The method was designed to discover logical rules including combination of GO terms in their premises in order to provide functional description of analyzed gene signatures. As the number of obtained rules is typically huge, filtration algorithm is required to select only the most interesting ones. Rule interestingness measures currently used within the RuleGO method do not always allow for the selection of the rules according to user's subjective preferences. In this paper we propose an application of the UTA method for estimation of the multicriteria rule interestingness measure reflecting expert's subjective rule evaluation. In the presented method, each of the rules is characterized by a vector of values reflecting its quality due to the different parial interestingness measures. From the designated set of rules a set of representative rules is selected and presented to an expert who orders the rules based on his preferences. Using the information about the order and values of the partial interestingness measures, the additive multicriteria interestingness measure is estimated. The measure is estimated in such a way that the rule ranking obtained by this function is consistent with the ranking given by an expert. The presented approach is applied to three microarray data sets and obtained rule orders are compared with rule orders generated with the standard RuleGO rule evaluation method. Presented method allows obtaining the rule ranking that is better correlated with expert ranking than the ranking obtained in the standard way.

4

Application of binary similarity measures to analysis of genes represented in gene ontology domain

Kozielski M., Gruca A.

Studia Informatica

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2012

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Vol. 33, nr 2A

543-554

EN

The work presents application of four binary similarity measures to analysis of Gene Ontology data. The measures are analysed and compared with semantic measure calculating term and gene similarity. Two kinds of experiments performed on two gene datasets show that binary similarity measures are valuable and interesting methods for the considered application.

PL

Artykuł przedstawia zastosowanie czterech binarnych miar podobieństwa do analizy danych z ontologii Gene Ontology. Miary są analizowane i porównywane z semantyczną miarą wyznaczającą podobieństwo genów na podstawie podobieństwa terminów ontologii. Przeprowadzone zostały dwa typy eksperymentów na dwóch zbiorach danych o różnej charakterystyce. Eksperymenty te pokazują, że binarne miary podobieństwa są wartościowymi i interesującymi metodami analizy dla opisywanego zastosowania.

5

Visual comparison of clustering gene ontology with different similarity measures

Kozielski M., Gruca A.

Studia Informatica

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2011

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Vol. 32, nr 2A

169-180

EN

The work presents comparison of four Gene Ontology term similarity measures combined with two methods calculating gene similarity on the basis of terms similarity. Visual comparison of clustering results, where different clustering methods were applied, indicates the best combination of similarity methods that can be utilised in a clustering process.

PL

W artykule przedstawiono porównanie czterech miar podobieństwa terminów ontologii genowych w połączeniu z dwoma miarami podobieństwa genów, przypisanych do tych terminów. Porównane zostały wizualne wyniki grupowania (takie, jak dendrogram), uzyskane za pomocą dwóch algorytmów różnego typu. Wyniki analizy pokazują, które połączenie miar podobieństwa niesie najwięcej informacji wykorzystywanej w procesie grupowania.

6

Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules

Sikora M., Gruca A.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 3

555-570

EN

In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.

7

Sequence similarity based method for protein function prediction

Blazewicz J., Lukasiak P., Milostan M., Krasnogor N., Jackowiak P.

Foundations of Computing and Decision Sciences

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2009

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Vol. 34, No. 3

173-191

EN

Motivation: Proteins are the main building blocks of life. They catalyze biological processes in living cells to sustain life and improve metabolism. They also act as biological scaffolds and are cell's workhorses. As a matter of fact, knowing their function is one of the most important milestones for understanding life.The function depends on the tertiary structure of the protein, but only for a fraction of amino acid sequences gathered in databases the structure is known. Thus, creation of efficient and accurate methods that predict function from sequences, based on already known function-sequence assignments, is a fundamental challenge in computational biology. Results: First, we show a detailed analysis of a usability of similarity search engines in the context of function prediction. Then we propose a simple and effective method for assigning function to sequences based on the results of similarity searches and information gathered from gene ontology annotation graphs. Availability: All data used for the analysis presented in this paper as well as raw result are available at the site: http://bio.cs.put.poznan.pl/funcpred/data/ Suplementary Material: Suplementary materials with additional charts are available at: http://bio.es.put.poznan.pl/funcpred/suplement/ Contact: protbio@cs.put.poznan.pl