{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:40:45Z","timestamp":1765438845852,"version":"3.41.2"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002070","82001331"],"award-info":[{"award-number":["62002070","82001331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Plan Project of Guangzhou City","award":["202102021236"],"award-info":[{"award-number":["202102021236"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA\u2013disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA\u2013disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA\u2013disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https:\/\/github.com\/RuiBingo\/SSMF-BLNP.<\/jats:p>","DOI":"10.1093\/bib\/bbac595","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T14:37:37Z","timestamp":1672670257000},"source":"Crossref","is-referenced-by-count":29,"title":["Predicting lncRNA\u2013disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation"],"prefix":"10.1093","volume":"24","author":[{"given":"Guo-Bo","family":"Xie","sequence":"first","affiliation":[{"name":"School of Computer, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Rui-Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Zhi-Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Guo-Sheng","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Jun-Rui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Zhen-guo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, 510080 , China"}]},{"given":"Ji","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, 510080 , China"}]},{"given":"Lie-qing","family":"Lin","sequence":"additional","affiliation":[{"name":"Center of Campus Network & Modern Educational Technology, Guangdong University of Technology , Guangzhou, 510000 , China"}]},{"given":"Lang-cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Center of Campus Network & Modern Educational Technology, Guangdong University of Technology , Guangzhou, 510000 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"2023011917141459200_ref1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.virusres.2018.08.018","article-title":"Lncrna, mirna and lncrna-mirna interaction in viral infection","volume":"257","author":"Chen","year":"2018","journal-title":"Virus Res"},{"issue":"2","key":"2023011917141459200_ref2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1093\/bib\/bbx130","article-title":"Micrornas and complex diseases: from experimental results to computational models","volume":"20","author":"Chen","year":"2019","journal-title":"Brief 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