Impact of the Continuous Evolution of Gene Ontology on Similarity Measures
| Autor: | Ashish Anand, Madhusudan Paul, Saptarshi Pyne |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Gene ontology Computer science business.industry 0206 medical engineering Robustness (evolution) 02 engineering and technology Similarity measure Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology Artificial intelligence Confidence score business computer 020602 bioinformatics Continuous evolution |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030348717 PReMI (2) |
| DOI: | 10.1007/978-3-030-34872-4_14 |
| Popis: | Gene Ontology (GO) is a taxonomy of biological terms related to the properties of genes and gene products. It can be used to define a similarity measure between two gene products and assign a confidence score to protein-protein interactions (PPIs). GO is being evolved regularly by the addition/deletion/merging of terms. However, there is no study which evaluates the robustness of a particular similarity measure over the evolution of GO. By robustness of a similarity measure, we mean it should either improve or keep its performance similar over the evolution of GO. In this paper, we systematically study the same for the task of scoring confidence of PPIs using GO-based similarity measures. We observe that the performance of similarity measures gets affected due to the regular updates of GO. We find that similarity measures are not robust in all conditions, rather they keep their performance quite similar over the evolution of GO in certain conditions. |
| Databáze: | OpenAIRE |
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