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@article{2257178, author = {Klimentová, Eva and Hejret, Václav and Krčmář, Ján and Grešová, Katarína and Giassa, IlektraandChara and Alexiou, Panagiotis}, article_number = {12}, doi = {http://dx.doi.org/10.3390/genes13122323}, keywords = {miRNA; target prediction; miRNA binding; CLASH; convolutional neural network}, language = {eng}, issn = {2073-4425}, journal = {GENES}, title = {miRBind: A Deep Learning Method for miRNA Binding Classification}, url = {https://www.mdpi.com/2073-4425/13/12/2323}, volume = {13}, year = {2022} }
TY - JOUR ID - 2257178 AU - Klimentová, Eva - Hejret, Václav - Krčmář, Ján - Grešová, Katarína - Giassa, Ilektra-Chara - Alexiou, Panagiotis PY - 2022 TI - miRBind: A Deep Learning Method for miRNA Binding Classification JF - GENES VL - 13 IS - 12 SP - 2323 EP - 2323 PB - MDPI SN - 20734425 KW - miRNA KW - target prediction KW - miRNA binding KW - CLASH KW - convolutional neural network UR - https://www.mdpi.com/2073-4425/13/12/2323 N2 - The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available. ER -
KLIMENTOVÁ, Eva, Václav HEJRET, Ján KRČMÁŘ, Katarína GREŠOVÁ, Ilektra-Chara GIASSA a Panagiotis ALEXIOU. miRBind: A Deep Learning Method for miRNA Binding Classification. \textit{GENES}. MDPI, 2022, roč.~13, č.~12, s.~2323-2335. ISSN~2073-4425. Dostupné z: https://dx.doi.org/10.3390/genes13122323.
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