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Accumulating evidence on\n                      <jats:italic>Human<\/jats:italic>\n                      diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA\u2013lncRNA interactions (GKLOMLI). Given an observed miRNA\u2013lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA\u2013lncRNA interactions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      To evaluate the performance of our proposed method,\n                      <jats:italic>k<\/jats:italic>\n                      -fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623\u2009\u00b1\u20090.0027 (2-fold CV), 0.9053\u2009\u00b1\u20090.0017 (5-fold CV), 0.9151\u2009\u00b1\u20090.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-023-05309-w","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T11:02:31Z","timestamp":1683543751000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["GKLOMLI: a link prediction model for inferring miRNA\u2013lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm"],"prefix":"10.1186","volume":"24","author":[{"given":"Leon","family":"Wong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-An","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Yuan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"issue":"5258","key":"5309_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1038\/227561a0","volume":"227","author":"F Crick","year":"1970","unstructured":"Crick F. 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