Abstract
Proteins encoded by the genes associated with a common disorder interact together, participate in similar pathways, and share Gene Ontology (GO) terms. Drug discovery for certain disease may arise from a hypothesis that genes contributing to a common disorder have an increased tendency for their products to be linked at various functional levels. This may be induced from experimental studies of protein-protein interactions, co-regulation, co-expression, and annotated semantic information (e.g., those stored in Gene Ontology). Our aim is to improve the quality of aggregation discovery in dense biological interactions by incorporating such information embedded in biological repositories and mapping them in the spectral embedding space.
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Mahmoud, H., Masulli, F., Rovetta, S. (2017). Semantic Clustering for Identifying Overlapping Biological Communities. In: Bracciali, A., Caravagna, G., Gilbert, D., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science(), vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-319-67834-4_19
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