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Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04457-1","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T07:03:14Z","timestamp":1635836594000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec"],"prefix":"10.1186","volume":"22","author":[{"given":"Jianwei","family":"Li","sequence":"first","affiliation":[]},{"given":"Jianing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mengfan","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Duanyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jiangcheng","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"issue":"7235","key":"4457_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1038\/nature07672","volume":"458","author":"M Guttman","year":"2009","unstructured":"Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, Huarte M, Zuk O, Carey BW, Cassady JP. 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