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Warianty tytułu
Metody normalizacji podobieństwa wyznaczonego dla terminów ontologii Gene Ontology
Języki publikacji
Abstrakty
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.
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.
Czasopismo
Rocznik
Tom
Strony
7--18
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
- Silesian University of Technology, Institute of Computer Science Future Processing Sp. z o.o.
autor
- Silesian University of Technology, Institute of Electronics
Bibliografia
- 1. Alvarez M. A., Qi X., Yan C.: A shortest-path graph kernel for estimating gene product semantic similarity. J. Biomedical Semantics, 2, 3, 2011.
- 2. Ashburner M. et al.: Gene Ontology: tool for the unification of biology. Nature genetics 25.1, 2000, p. 25÷29.
- 3. Azuaje F., Wang H., Bodenreider O.: Ontology-driven similarity approaches to supporting gene functional assessment. Proceedings Of The Eighth Annual Bio-Ontologies Meeting, Michigan 2005, p. 9÷10.
- 4. Cho R. J., Campbell M. J., Winzeler E. A., Steinmetz L., Conway A., Wodicka L., Wolfs-berg T. G., Gabrielian A. E., Landsman D., Lockhart D. J., Davis, R. W.: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2, 1998, p. 65÷73.
- 5. Couto F. M., Silva M. J., Coutinho, P. M.: Measuring semantic similarity between Gene Ontology terms. Data & knowledge engineering, 61(1), 2007, p. 137÷152.
- 6. Eisen M. B., Spellman P. T., Brown P. O., Botstein D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 1998, p. 14863÷14868.
- 7. GO-Consortium: The Gene Ontology (GO) database and informatics resource, Nucleic Acids Research, 32, 2004 (http://www.geneontology.org).
- 8. Iyer V. R., Eisen M. B., Ross D. T., Schuler G., Moore T., Lee J. C., Trent J. M., Staudt L. M., Hudson J., Boguski M., Lashkari D., Shalon D., Botstein D., Brown P.: The transcriptional program in the response of human fibroblasts to serum. Science, 283, 1999, p. 83÷87.
- 9. Jain S., Bader G.: An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology. BMC Bioinformatics, 11(1), 2010, 562.
- 10. Jiang J. J., Conrath D. W.: Semantic similarity based on corpus statistics and lexical ontology. Proc. on International Conference on Research in Computational Linguistics, 1997, p. 19÷33.
- 11. Kozielski M., Gruca A.: Evaluation of semantic term and gene similarity measures. Pattern Recognition and Machine Intelligence, 2011, p. 406÷411.
- 12. Lin D:. An information-theoretic definition of similarity. Proc. of the 15th Int'l Conference on Machine Learning, 1998, p. 296÷304.
- 13. Al Mubaid H., Nagar A.: Comparison of four similarity measures based on GO annotations for gene clustering. Computers and Communications. ISCC 2008. IEEE Symposium, 2008, p. 531÷536.
- 14. Pesquita C., Faria D., Falcão A. O., Lord P., Couto F. M.: Semantic Similarity in Biomedical Ontologies. PLoS Comput Biol 5(7), 2009, p. 1÷12.
- 15. Resnik P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. J. Artif. Intell. Res. (JAIR), Vol. 11, 1999, p. 95÷130.
- 16. Sevilla J. L., Segura V., Podhorski A., Guruceaga E., Mato J. M., Martinez-Cruz L. A., Corrales F. J., Rubio A.: Correlation between gene expression and GO semantic similarity. IEEE/ACM Trans. on Computational Biology and Bioinformatics, 2(4), 2005, p. 330÷338.
- 17. Wang H., Azuaje F., Bodenreider O., Dopazo J.: Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships. Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '04, 2004, p. 25÷31
- 18. Yang H., Nepusz T., Paccanaro A.: Improving GO semantic similarity measures by exploring the ontology beneath the terms and modelling uncertainty. Bioinformatics, 28(10), 2012, p. 1383÷1389.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4b553ff2-6ae2-4ba0-b23b-0f8bbbbfc036