{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:59:09Z","timestamp":1769633949345,"version":"3.49.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["18H04113"],"award-info":[{"award-number":["18H04113"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["108-2636-B-009-005"],"award-info":[{"award-number":["108-2636-B-009-005"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010697","name":"Institute for Chemical Research, Kyoto University","doi-asserted-by":"publisher","award":["2020-25"],"award-info":[{"award-number":["2020-25"]}],"id":[{"id":"10.13039\/501100010697","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Human dicer is an enzyme that cleaves pre-miRNAs into miRNAs. Several models have been developed to predict human dicer cleavage sites, including PHDCleav and LBSizeCleav. Given an input sequence, these models can predict whether the sequence contains a cleavage site. However, these models only consider each sequence independently and lack interpretability. Therefore, it is necessary to develop an accurate and explainable predictor, which employs relations between different sequences, to enhance the understanding of the mechanism by which human dicer cleaves pre-miRNA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we develop an accurate and explainable predictor for human dicer cleavage site \u2013 ReCGBM. We design relational features and class features as inputs to a lightGBM model. Computational experiments show that ReCGBM achieves the best performance compared to the existing methods. Further, we find that features in close proximity to the center of pre-miRNA are more important and make a significant contribution to the performance improvement of the developed method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The results of this study show that ReCGBM is an interpretable and accurate predictor. Besides, the analyses of feature importance show that it might be of particular interest to consider more informative features close to the center of the pre-miRNA in future predictors.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-03993-0","type":"journal-article","created":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T08:05:31Z","timestamp":1613030731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ReCGBM: a gradient boosting-based method for predicting human dicer cleavage sites"],"prefix":"10.1186","volume":"22","author":[{"given":"Pengyu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[]},{"given":"Chun-Yu","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-797X","authenticated-orcid":false,"given":"Tatsuya","family":"Akutsu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,10]]},"reference":[{"key":"3993_CR1","unstructured":"Tanase C, Ogrezeanu I, Badiu C, Heidelberg L. Molecular Pathology of Pituitary Adenomas. vol.\u00a08; 2012."},{"issue":"16","key":"3993_CR2","doi-asserted-by":"publisher","first-page":"7065","DOI":"10.1158\/0008-5472.CAN-05-1783","volume":"65","author":"MV Iorio","year":"2005","unstructured":"Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65(16):7065\u201370.","journal-title":"Cancer Res"},{"issue":"11","key":"3993_CR3","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1158\/0008-5472.CAN-04-0637","volume":"64","author":"J Takamizawa","year":"2004","unstructured":"Takamizawa J, Konishi H, Yanagisawa K, Tomida S, Osada H, Endoh H, et al. Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival. Cancer Res. 2004;64(11):3753\u20136.","journal-title":"Cancer Res"},{"issue":"52","key":"3993_CR4","doi-asserted-by":"publisher","first-page":"19075","DOI":"10.1073\/pnas.0509603102","volume":"102","author":"H He","year":"2005","unstructured":"He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, et al. The role of microRNA genes in papillary thyroid carcinoma. Proc Nat Acad Sci. 2005;102(52):19075\u201380.","journal-title":"Proc Nat Acad Sci"},{"issue":"6","key":"3993_CR5","doi-asserted-by":"publisher","first-page":"793","DOI":"10.3390\/cancers11060793","volume":"11","author":"P Galka-Marciniak","year":"2019","unstructured":"Galka-Marciniak P, Urbanek-Trzeciak MO, Nawrocka PM, Dutkiewicz A, Giefing M, Lewandowska MA, et al. Somatic mutations in miRNA genes in lung cancer-potential functional consequences of non-coding sequence variants. Cancers. 2019;11(6):793.","journal-title":"Cancers"},{"key":"3993_CR6","doi-asserted-by":"crossref","unstructured":"Wee LJ, Tan TW, Ranganathan S. SVM-based prediction of caspase substrate cleavage sites. In: BMC bioinformatics. vol.\u00a07. Springer; 2006. p. S14.","DOI":"10.1186\/1471-2105-7-S5-S14"},{"issue":"23","key":"3993_CR7","doi-asserted-by":"publisher","first-page":"3241","DOI":"10.1093\/bioinformatics\/btm334","volume":"23","author":"LJ Wee","year":"2007","unstructured":"Wee LJ, Tan TW, Ranganathan S. CASVM: web server for SVM-based prediction of caspase substrates cleavage sites. Bioinformatics. 2007;23(23):3241\u20133.","journal-title":"Bioinformatics"},{"issue":"5","key":"3993_CR8","doi-asserted-by":"publisher","first-page":"e19035","DOI":"10.1371\/journal.pone.0019035","volume":"6","author":"Y Ono","year":"2011","unstructured":"Ono Y, Sorimachi H, Mamitsuka H, et al. Calpain cleavage prediction using multiple kernel learning. PLoS ONE. 2011;6(5):e19035.","journal-title":"PLoS ONE"},{"issue":"1","key":"3993_CR9","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1186\/1471-2105-11-320","volume":"11","author":"M Piippo","year":"2010","unstructured":"Piippo M, Lietz\u00e9n N, Nevalainen OS, Salmi J, Nyman TA. Pripper: prediction of caspase cleavage sites from whole proteomes. BMC Bioinform. 2010;11(1):320.","journal-title":"BMC Bioinform"},{"issue":"6","key":"3993_CR10","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1093\/bioinformatics\/btq043","volume":"26","author":"J Song","year":"2010","unstructured":"Song J, Tan H, Shen H, Mahmood K, Boyd SE, Webb GI, et al. Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics. 2010;26(6):752\u201360.","journal-title":"Bioinformatics"},{"issue":"11","key":"3993_CR11","doi-asserted-by":"publisher","first-page":"e50300","DOI":"10.1371\/journal.pone.0050300","volume":"7","author":"J Song","year":"2012","unstructured":"Song J, Tan H, Perry AJ, Akutsu T, Webb GI, Whisstock JC, et al. PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites. PLoS ONE. 2012;7(11):e50300.","journal-title":"PLoS ONE"},{"issue":"1","key":"3993_CR12","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/bioinformatics\/btt603","volume":"30","author":"M Wang","year":"2014","unstructured":"Wang M, Zhao XM, Tan H, Akutsu T, Whisstock JC, Song J. Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets. Bioinformatics. 2014;30(1):71\u201380.","journal-title":"Bioinformatics"},{"issue":"17","key":"3993_CR13","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1186\/s12859-016-1337-6","volume":"17","author":"O Singh","year":"2016","unstructured":"Singh O, Su ECY. Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features. BMC Bioinformatics. 2016;17(17):478.","journal-title":"BMC Bioinformatics"},{"key":"3993_CR14","doi-asserted-by":"publisher","first-page":"715","DOI":"10.3389\/fgene.2019.00715","volume":"10","author":"Z Liu","year":"2019","unstructured":"Liu Z, Yu K, Dong J, Zhao L, Liu Z, Zhang Q, et al. Precise prediction of calpain cleavage sites and their aberrance caused by mutations in cancer. Front Genet. 2019;10:715.","journal-title":"Front Genet"},{"issue":"4","key":"3993_CR15","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1002\/prot.24217","volume":"81","author":"YX Fan","year":"2013","unstructured":"Fan YX, Zhang Y, Shen HB. LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields. Proteins Struct Funct Bioinf. 2013;81(4):622\u201334.","journal-title":"Proteins Struct Funct Bioinf"},{"key":"3993_CR16","doi-asserted-by":"crossref","unstructured":"Ahmed F, Kaundal R, Raghava GP. PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors. In: BMC bioinformatics. vol.\u00a014. BioMed Central; 2013. p.\u00a0S9.","DOI":"10.1186\/1471-2105-14-S14-S9"},{"issue":"1","key":"3993_CR17","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1186\/s12859-016-1353-6","volume":"17","author":"Y Bao","year":"2016","unstructured":"Bao Y, Hayashida M, Akutsu T. LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop\/bulge length. BMC Bioinform. 2016;17(1):487.","journal-title":"BMC Bioinform"},{"key":"3993_CR18","doi-asserted-by":"crossref","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Satist. 2001;p. 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"issue":"suppl-1","key":"3993_CR19","doi-asserted-by":"publisher","first-page":"D154","DOI":"10.1093\/nar\/gkm952","volume":"36","author":"S Griffiths-Jones","year":"2007","unstructured":"Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2007;36(suppl-1):D154\u20138.","journal-title":"Nucleic Acids Res"},{"key":"3993_CR20","doi-asserted-by":"crossref","unstructured":"Markham N, Zuker M, Keith J. UNAFold: software for nucleic acid folding and hybridization., pp. 3\u201331. Humana Press,Totowa, NJ; 2008.","DOI":"10.1007\/978-1-60327-429-6_1"},{"issue":"13","key":"3993_CR21","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1093\/nar\/gkg599","volume":"31","author":"IL Hofacker","year":"2003","unstructured":"Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res. 2003;31(13):3429\u201331.","journal-title":"Nucleic Acids Res"},{"issue":"5814","key":"3993_CR22","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972\u20136.","journal-title":"Science"},{"key":"3993_CR23","first-page":"707","volume":"10","author":"VI Levenshtein","year":"1996","unstructured":"Levenshtein VI. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Phys Doklady. 1996;10:707\u201310.","journal-title":"Soviet Phys Doklady"},{"key":"3993_CR24","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems. 2017;3146\u201354."},{"key":"3993_CR25","unstructured":"Ranka S, Singh V. CLOUDS: A decision tree classifier for large datasets. In: Proceedings of the 4th knowledge discovery and data mining conference. vol. 2; 1998. ."},{"key":"3993_CR26","doi-asserted-by":"crossref","unstructured":"Jin R, Agrawal G. Communication and memory efficient parallel decision tree construction. In: Proceedings of the 2003 SIAM international conference on data mining. SIAM; 2003. p. 119\u2013129.","DOI":"10.1137\/1.9781611972733.11"},{"key":"3993_CR27","unstructured":"Li P, Wu Q, Burges CJ. Mcrank: Learning to rank using multiple classification and gradient boosting. In: Advances in neural information processing systems; 2008. p. 897\u2013904."},{"issue":"Oct","key":"3993_CR28","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12(Oct):2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"W1","key":"3993_CR29","doi-asserted-by":"publisher","first-page":"W471","DOI":"10.1093\/nar\/gkt290","volume":"41","author":"S Bellaousov","year":"2013","unstructured":"Bellaousov S, Reuter JS, Seetin MG, Mathews DH. RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res. 2013;41(W1):W471\u20134.","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"3993_CR30","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1021\/bi300755u","volume":"52","author":"CW Leonard","year":"2013","unstructured":"Leonard CW, Hajdin CE, Karabiber F, Mathews DH, Favorov OV, Dokholyan NV, et al. Principles for understanding the accuracy of SHAPE-directed RNA structure modeling. Biochemistry. 2013;52(4):588\u201395.","journal-title":"Biochemistry"},{"issue":"6","key":"3993_CR31","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1101\/gr.849004","volume":"14","author":"GE Crooks","year":"2004","unstructured":"Crooks GE, Hon G, Chandonia JM, Brenner SE. WebLogo: a sequence logo generator. Genome Res. 2004;14(6):1188\u201390.","journal-title":"Genome Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-03993-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-021-03993-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-03993-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T08:06:51Z","timestamp":1613030811000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-03993-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,10]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["3993"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-03993-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,10]]},"assertion":[{"value":"10 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"TA and JS are Associate Editors of BMC Bioinformatics.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"63"}}