{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:11:14Z","timestamp":1783152674118,"version":"3.54.6"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"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":["U21B2009"],"award-info":[{"award-number":["U21B2009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271049"],"award-info":[{"award-number":["62271049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["JQ19019"],"award-info":[{"award-number":["JQ19019"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http:\/\/bliulab.net\/GraLTR-LDA.<\/jats:p>","DOI":"10.1093\/bib\/bbac539","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T08:52:09Z","timestamp":1671699129000},"source":"Crossref","is-referenced-by-count":24,"title":["LncRNA-disease association identification using graph auto-encoder and learning to rank"],"prefix":"10.1093","volume":"24","author":[{"given":"Qi","family":"Liang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology School of Computer Science and Technology, , Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology School of Computer Science and Technology, , Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology School of Computer Science and Technology, , Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology School of Computer Science and Technology, , Beijing 100081, China"},{"name":"Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Science, , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"2023011917142925200_ref1","doi-asserted-by":"crossref","DOI":"10.1016\/j.biopha.2020.111158","article-title":"Role of lncRNA LUCAT1 in cancer","volume":"134","author":"Xing","year":"2021","journal-title":"Biomed Pharmacother"},{"key":"2023011917142925200_ref2","doi-asserted-by":"crossref","first-page":"D983","DOI":"10.1093\/nar\/gks1099","article-title":"LncRNADisease: a database for long-non-coding RNA-associated diseases","volume":"41","author":"Chen","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023011917142925200_ref3","doi-asserted-by":"crossref","first-page":"D1251","DOI":"10.1093\/nar\/gkaa1006","article-title":"Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA\/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data","volume":"49","author":"Gao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023011917142925200_ref4","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.2174\/1574893616666210712091221","article-title":"Prediction of lncRNA-disease associations based on robust multi-label learning","volume":"16","author":"Zhang","year":"2021","journal-title":"Current Bioinformatics"},{"key":"2023011917142925200_ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104028","article-title":"Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)","volume":"127","author":"Ramakrishnaiah","year":"2020","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bfgp\/elaa023","article-title":"Prediction of bio-sequence modifications and the associations with diseases","volume":"20","author":"Ao","year":"2021","journal-title":"Brief Funct Genomics"},{"key":"2023011917142925200_ref7","first-page":"558","article-title":"Long non-coding RNAs and complex diseases: from experimental results to computational models","volume":"18","author":"Chen","year":"2017","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref8","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1093\/bfgp\/ely031","article-title":"Computational models for lncRNA function prediction and functional similarity calculation","volume":"18","author":"Chen","year":"2019","journal-title":"Brief Funct Genomics"},{"key":"2023011917142925200_ref9","doi-asserted-by":"crossref","first-page":"371","DOI":"10.2174\/1574893615999200715165335","article-title":"Fusing multiple biological networks to effectively predict miRNA-disease associations","volume":"16","author":"Zhu","year":"2021","journal-title":"Current Bioinformatics"},{"key":"2023011917142925200_ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104695","article-title":"Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study","volume":"136","author":"Saxena","year":"2021","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref11","doi-asserted-by":"crossref","first-page":"524","DOI":"10.2174\/1574893615999200801014239","article-title":"A constrained probabilistic matrix decomposition method for predicting miRNA-disease associations","volume":"16","author":"Lu","year":"2021","journal-title":"Current Bioinformatics"},{"key":"2023011917142925200_ref12","doi-asserted-by":"crossref","first-page":"710","DOI":"10.2174\/1574893616999210120181506","article-title":"MDAPlatform: a component-based platform for constructing and assessing miRNA-disease association prediction methods","volume":"16","author":"Zhang","year":"2021","journal-title":"Current Bioinformatics"},{"key":"2023011917142925200_ref13","doi-asserted-by":"crossref","first-page":"104649","DOI":"10.1016\/j.compbiomed.2021.104649","article-title":"DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques","volume":"136","author":"Rahaman","year":"2021","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref14","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1093\/bioinformatics\/btt426","article-title":"Novel human lncRNA-disease association inference based on lncRNA expression profiles","volume":"29","author":"Chen","year":"2013","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref15","doi-asserted-by":"crossref","first-page":"58849","DOI":"10.1109\/ACCESS.2019.2914533","article-title":"Prediction of LncRNA-disease associations based on network consistency projection","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"2023011917142925200_ref16","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1093\/bioinformatics\/bty327","article-title":"Prediction of lncRNA-disease associations based on inductive matrix completion","volume":"34","author":"Lu","year":"2018","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref17","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/TCBB.2020.3020595","article-title":"LDA-LNSUBRW: lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk","volume":"19","author":"Xie","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917142925200_ref18","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.isci.2019.08.030","article-title":"A learning-based method for LncRNA-disease association identification combing similarity information and rotation forest","volume":"19","author":"Guo","year":"2019","journal-title":"iScience"},{"key":"2023011917142925200_ref19","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1186\/s12859-020-03721-0","article-title":"LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting","volume":"21","author":"Zhang","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2023011917142925200_ref20","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s12859-021-04104-9","article-title":"IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier","volume":"22","author":"Zhu","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2023011917142925200_ref21","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1109\/TCBB.2020.2983958","article-title":"DMFLDA: a deep learning framework for predicting lncRNA-disease associations","volume":"18","author":"Zeng","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917142925200_ref22","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/TCBB.2020.2964221","article-title":"iLncRNAdis-FB: identify lncRNA-disease associations by fusing biological feature blocks through deep neural network","volume":"18","author":"Wei","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917142925200_ref23","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf"},{"key":"2023011917142925200_ref24","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/s12859-021-04073-z","article-title":"A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations","volume":"22","author":"Shi","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2023011917142925200_ref25","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab361","article-title":"GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field","volume":"23","author":"Fan","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref26","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2020.09.094","article-title":"GANLDA: Graph attention network for lncRNA-disease associations prediction","volume":"469","author":"Lan","year":"2022","journal-title":"Neurocomputing"},{"key":"2023011917142925200_ref27","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1093\/bib\/bbz159","article-title":"NCMCMDA: miRNA-disease association prediction through neighborhood constraint matrix completion","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref28","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa186","article-title":"Deep-belief network for predicting potential miRNA-disease associations","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007209","article-title":"Ensemble of decision tree reveals potential miRNA-disease associations","volume":"15","author":"Chen","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2023011917142925200_ref30","first-page":"113","article-title":"Learning to rank for information retrieval and natural language processing","volume":"4","author":"Li","year":"2014","journal-title":"Synthesis Lectures on Human Language Technologies"},{"key":"2023011917142925200_ref31","first-page":"177","article-title":"Discriminative reranking for machine translation","volume":"77","author":"Shen","year":"2004","journal-title":"In HLT-NAACL"},{"key":"2023011917142925200_ref32","first-page":"4107","article-title":"Improving entity recommendation with search log and multi-task learning","author":"Huang","year":"2018","journal-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"},{"key":"2023011917142925200_ref33","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1093\/bioinformatics\/btaa284","article-title":"HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref34","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1093\/bioinformatics\/btv413","article-title":"Application of learning to rank to protein remote homology detection","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref35","doi-asserted-by":"crossref","first-page":"102499","DOI":"10.1109\/ACCESS.2019.2929363","article-title":"ProtDec-LTR3.0: protein remote homology detection by incorporating profile-based features into learning to rank, IEEE","volume":"7","author":"Liu","year":"2019","journal-title":"Access"},{"key":"2023011917142925200_ref36","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2021.3108168","article-title":"ProtRe-CN: protein remote homology detection by combining classification methods and network methods via learning to rank","author":"Shao","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917142925200_ref37","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btac048","article-title":"NerLTR-DTA: Drug-target binding affinity prediction based on neighbor relationship and learning to rank","volume":"38","author":"Ru","year":"2022","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref38","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105605","article-title":"iLncDA-LTR: Identification of lncRNA-disease associations by learning to rank","volume":"146","author":"Wu","year":"2022","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref39","article-title":"Variational graph auto-encoders","author":"Kipf"},{"key":"2023011917142925200_ref40","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.neunet.2020.08.021","article-title":"MGAT: multi-view graph attention networks","volume":"132","author":"Xie","year":"2020","journal-title":"Neural Netw"},{"key":"2023011917142925200_ref41","doi-asserted-by":"crossref","first-page":"D733","DOI":"10.1093\/nar\/gkv1189","article-title":"Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation","volume":"44","author":"O'Leary","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023011917142925200_ref42","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/0022-2836(70)90057-4","article-title":"A general method applicable to the search for similarities in the amino acid sequence of two proteins","volume":"48","author":"Needleman","year":"1970","journal-title":"J Mol Biol"},{"key":"2023011917142925200_ref43","doi-asserted-by":"crossref","first-page":"D1071","DOI":"10.1093\/nar\/gku1011","article-title":"Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data","volume":"43","author":"Kibbe","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023011917142925200_ref44","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1093\/bioinformatics\/btu684","article-title":"DOSE: an R\/Bioconductor package for disease ontology semantic and enrichment analysis","volume":"31","author":"Yu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref45","doi-asserted-by":"crossref","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","article-title":"Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction","volume":"127","author":"Jiang","year":"2020","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref46","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1109\/JBHI.2021.3079302","article-title":"Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder","volume":"25","author":"Hao","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"2023011917142925200_ref47","article-title":"Adam: a method for stochastic optimization","author":"Kingma"},{"key":"2023011917142925200_ref48","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104742","article-title":"Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification","volume":"136","author":"La Salvia","year":"2021","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref49","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa391","article-title":"GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest","volume":"22","author":"Wu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref50","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab604","article-title":"Multi-channel graph attention autoencoders for disease-related lncRNAs prediction","volume":"23","author":"Sheng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref51","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa394","article-title":"Application of learning to rank in bioinformatics tasks","volume":"22","author":"Ru","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref52","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103660","article-title":"Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm","volume":"119","author":"Ru","year":"2020","journal-title":"Comput Biol Med"},{"key":"2023011917142925200_ref53","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btab334","article-title":"iCircDA-LTR: identification of circRNA-disease associations based on Learning to Rank","volume":"37","author":"Wei","year":"2021","journal-title":"Bioinformatics"},{"key":"2023011917142925200_ref54","first-page":"81","article-title":"From ranknet to lambdarank to lambdamart: An overview","volume":"11","author":"Burges","year":"2010","journal-title":"Learning"},{"key":"2023011917142925200_ref55","first-page":"243","article-title":"IR evaluation methods for retrieving highly relevant documents","volume-title":"ACM SIGIR Forum","author":"J\u00e4rvelin","year":"2017"},{"key":"2023011917142925200_ref56","doi-asserted-by":"crossref","first-page":"D1034","DOI":"10.1093\/nar\/gky905","article-title":"LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases","volume":"47","author":"Bao","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023011917142925200_ref57","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab407","article-title":"Heterogeneous graph attention network based on meta-paths for lncRNA\u2013disease association prediction","volume-title":"Brief Bioinform","author":"Zhao","year":"2022"},{"key":"2023011917142925200_ref58","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac429","article-title":"ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm","author":"Zhao","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917142925200_ref59","first-page":"25","article-title":"Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching","volume-title":"Computers & chemistry","author":"Gribskov","year":"1996"},{"key":"2023011917142925200_ref60","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1186\/s12935-020-01697-8","article-title":"The interplay between ATF2 and NEAT1 contributes to lung adenocarcinoma progression","volume":"20","author":"Liu","year":"2020","journal-title":"Cancer Cell Int"},{"key":"2023011917142925200_ref61","first-page":"1480","article-title":"The PVT1\/miR-612\/CENP-H\/CDK1 axis promotes malignant progression of advanced endometrial cancer","volume":"11","author":"Cong","year":"2021","journal-title":"Am J Cancer Res"},{"key":"2023011917142925200_ref62","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/JAS.2021.1004198","article-title":"A distributed framework for large-scale protein-protein interaction data analysis and prediction using MapReduce","volume":"9","author":"Hu","year":"2022","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"2023011917142925200_ref63","article-title":"HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks","volume":"23","author":"Zhao","year":"2022","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac539\/48783142\/bbac539.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac539\/48783142\/bbac539.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T22:25:50Z","timestamp":1728339950000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac539\/6955271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac539","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2022,12,21]]},"article-number":"bbac539"}}