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They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA\u2013mRNA interactions from raw data use one-dimensional miRNA\u2013mRNA duplex representations and simple sequence encoding techniques, which may limit their performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We have developed GraphTar, a new target prediction method that uses a novel graph-based representation to reflect the spatial structure of the miRNA\u2013mRNA duplex. Unlike existing approaches, we use the word2vec method to accurately encode RNA sequence information. In conjunction with the novel encoding method, we use a graph neural network classifier that can accurately predict miRNA\u2013mRNA interactions based on graph representation learning. As part of a comparative study, we evaluate three different node embedding approaches within the GraphTar framework and compare them with other state-of-the-art target prediction methods. The results show that the proposed method achieves similar performance to the best methods in the field and outperforms them on one of the datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, a novel miRNA target prediction approach called GraphTar is introduced. Results show that GraphTar is as effective as existing methods and even outperforms them in some cases, opening new avenues for further research. However, the expansion of available datasets is critical for advancing the field towards real-world applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-023-05564-x","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T08:01:52Z","timestamp":1700208112000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["GraphTar: applying word2vec and graph neural networks to miRNA target prediction"],"prefix":"10.1186","volume":"24","author":[{"given":"Jan","family":"Przybyszewski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maciej","family":"Malawski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabina","family":"Licho\u0142ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"issue":"5","key":"5564_CR1","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/0092-8674(93)90529-Y","volume":"75","author":"RC Lee","year":"1993","unstructured":"Lee RC, Feinbaum RL, Ambros V. 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