Abstract
In recent years, ontologies have been extensively used in many biological fields to support a variety of applications. A well known example is Gene Ontology (GO) that organizes a vocabulary of terms about gene products and functions. GO offers an effective support for evaluating the similarity between two genes by measuring the distance of their respective GO terms. The advent of high-throughput technologies and the consequent production of lists of genes associated with specific conditions is stressing the need of recognizing groups of genes which cooperate within a specific biological event. This paper compares six popular similarity measures on GO in order to evaluate their effectiveness in discovering functionally coherent genes from an assigned list of genes. The aim is to discover which measure performs best. We also investigate about the potential of GO in evaluating the similarity of a set of genes according to its cardinality and the characteristics of the similarity measures. Experiments take into consideration: (a) 84 groups of genes sharing similar molecular functions through the production of enzymes within the human organism; (b) 150 groups of randomly selected genes. The paper demonstrates the efficient support of GO in detecting functionally related groups of genes, despite the GO’s hierarchical structure limits the representation of richer forms of knowledge.
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Dessì, N., Pes, B. (2016). The Effectiveness of Gene Ontology in Assessing Functionally Coherent Groups of Genes: A Case Study. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_24
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DOI: https://doi.org/10.1007/978-3-319-42007-3_24
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