{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:46:18Z","timestamp":1769748378516,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071085"],"award-info":[{"award-number":["62071085"]}],"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":["61801081"],"award-info":[{"award-number":["61801081"]}],"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":["61671107"],"award-info":[{"award-number":["61671107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Talent Introduction and Education Program of Shandong Educational Committee"},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QF143"],"award-info":[{"award-number":["ZR2021QF143"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Talent Introduction Project of Dezhou University","award":["2020xjrc216"],"award-info":[{"award-number":["2020xjrc216"]}]},{"name":"Enterprise Project","award":["HXKT2022003"],"award-info":[{"award-number":["HXKT2022003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Long non-coding RNAs (lncRNAs) played essential roles in nearly every biological process and disease. Many algorithms were developed to distinguish lncRNAs from mRNAs in transcriptomic data and facilitated discoveries of more than 600\u00a0000 of lncRNAs. However, only a tiny fraction (&amp;lt;1%) of lncRNA transcripts (~4000) were further validated by low-throughput experiments (EVlncRNAs). Given the cost and labor-intensive nature of experimental validations, it is necessary to develop computational tools to prioritize those potentially functional lncRNAs because many lncRNAs from high-throughput sequencing (HTlncRNAs) could be resulted from transcriptional noises. Here, we employed deep learning algorithms to separate EVlncRNAs from HTlncRNAs and mRNAs. For overcoming the challenge of small datasets, we employed a three-layer deep-learning neural network (DNN) with a K-mer feature as the input and a small convolutional neural network (CNN) with one-hot encoding as the input. Three separate models were trained for human (h), mouse (m) and plant (p), respectively. The final concatenated models (EVlncRNA-Dpred (h), EVlncRNA-Dpred (m) and EVlncRNA-Dpred (p)) provided substantial improvement over a previous model based on support-vector-machines (EVlncRNA-pred). For example, EVlncRNA-Dpred (h) achieved 0.896 for the area under receiver-operating characteristic curve, compared with 0.582 given by sequence-based EVlncRNA-pred model. The models developed here should be useful for screening lncRNA transcripts for experimental validations. EVlncRNA-Dpred is available as a web server at https:\/\/www.sdklab-biophysics-dzu.net\/EVlncRNA-Dpred\/index.html, and the data and source code can be freely available along with the web server.<\/jats:p>","DOI":"10.1093\/bib\/bbac583","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T10:54:20Z","timestamp":1672138460000},"source":"Crossref","is-referenced-by-count":10,"title":["EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4824-347X","authenticated-orcid":false,"given":"Bailing","family":"Zhou","sequence":"first","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maolin","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Feng","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baohua","family":"Ji","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Huang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junye","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Yu","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zanxia","family":"Cao","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute for Systems and Physical Biology, Shenzhen Bay Laboratory , Shenzhen 518055 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihua","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou 253023 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"2023011917103346900_ref1","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41580-020-00315-9","article-title":"Gene regulation by long non-coding RNAs and its biological functions","volume":"22","author":"Statello","year":"2021","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2023011917103346900_ref2","doi-asserted-by":"crossref","first-page":"D165","DOI":"10.1093\/nar\/gkaa1046","article-title":"NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants","volume":"49","author":"Zhao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023011917103346900_ref3","doi-asserted-by":"crossref","first-page":"W516","DOI":"10.1093\/nar\/gkz400","article-title":"CNIT: a fast and accurate web tool for identifying protein-coding and long non-coding transcripts based on intrinsic sequence composition","volume":"47","author":"Guo","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023011917103346900_ref4","doi-asserted-by":"crossref","first-page":"W12","DOI":"10.1093\/nar\/gkx428","article-title":"CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features","volume":"45","author":"Kang","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023011917103346900_ref5","doi-asserted-by":"crossref","first-page":"e74","DOI":"10.1093\/nar\/gkt006","article-title":"CPAT: coding-potential assessment tool using an alignment-free logistic regression 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