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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

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.

3

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.