Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/117951
Title: DIANA-microT web server : elucidating microRNA functions through target prediction
Authors: Maragkakis, Manolis
Reczko, Martin
Simossis, Victor A.
Alexiou, Panagiotis
Papadopoulos, Giorgos L.
Dalamagas, Theodore
Giannopoulos, Giorgos
Goumas, G.
Koukis, Evangelos
Kourtis, Kornilios
Vergoulis, Thanasis
Koziris, Nectarios
Sellis, Timoleon
Tsanakas, Panayotis
Hatzigeorgiou, Artemis G.
Keywords: MicroRNA
Visualization
Deep learning (Machine learning)
Information storage and retrieval systems
Issue Date: 2009
Publisher: Oxford University Press
Citation: Maragkakis, M., Reczko, M., Simossis, V. A., Alexiou, P., Papadopoulos, G. L., Dalamagas, T.,...Hatzigeorgiou, A. G. (2009). DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic acids research, 37(suppl_2), W273-W276.
Abstract: Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.
URI: https://www.um.edu.mt/library/oar/handle/123456789/117951
Appears in Collections:Scholarly Works - FacHScABS

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