{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:53:05Z","timestamp":1740135185413,"version":"3.37.3"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["(2016YFA0501600)"],"award-info":[{"award-number":["(2016YFA0501600)"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by <jats:italic>C<\/jats:italic>-features of siRNAs, <jats:italic>C<\/jats:italic>-features are generated by <jats:italic>MG<\/jats:italic>-algorithm, <jats:italic>Icc<\/jats:italic>-cluster and the different combinations of some commonly used features, <jats:italic>MG<\/jats:italic>-algorithm and <jats:italic>Icc<\/jats:italic>-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by <jats:italic>I<\/jats:italic>-iteration, where <jats:italic>I<\/jats:italic>-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04867-9","type":"journal-article","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T10:02:42Z","timestamp":1660384962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features"],"prefix":"10.1186","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6237-0459","authenticated-orcid":false,"given":"Xingang","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuhong","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuhong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"issue":"4","key":"4867_CR1","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s12041-010-0073-3","volume":"89","author":"SA Angaji","year":"2010","unstructured":"Angaji SA, Hedayati SS, Poor RH, Madani S, Poor SS, Panahi S. Application of RNA interference in treating human diseases. J Genet. 2010;89(4):527\u201337.","journal-title":"J Genet"},{"issue":"5","key":"4867_CR2","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1038\/nrg2968","volume":"12","author":"BL Davidson","year":"2011","unstructured":"Davidson BL, McCray PJ. Current prospects for RNA interference-based therapies. Nat Rev Genet. 2011;12(5):329\u201340.","journal-title":"Nat Rev Genet"},{"issue":"12","key":"4867_CR3","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1038\/nbt1369","volume":"23","author":"J Haasnoot","year":"2007","unstructured":"Haasnoot J, Westerhout EM, Berkhout B. RNA interference against viruses: strike and counterstrike. Nat Biotechnol. 2007;23(12):1435\u201343.","journal-title":"Nat Biotechnol"},{"issue":"4","key":"4867_CR4","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1038\/nrg2323","volume":"9","author":"S Duffy","year":"2008","unstructured":"Duffy S, Shackelton LA, Holmes EC. Rates of evolutionary change in viruses: patterns and determinants. Nat Rev Genet. 2008;9(4):267\u201376.","journal-title":"Nat Rev Genet"},{"key":"4867_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1038\/s41580-020-0253-9","volume":"21","author":"K Baumann","year":"2020","unstructured":"Baumann K. How plants silence stress. Nat Rev Mol Cell Biol. 2020;21:303.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"4867_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-020-2231-y","volume":"581","author":"H Wu","year":"2020","unstructured":"Wu H, Li B, Iwakawa HO, Pan Y, Tang X, Ling-Hu Q, Liu Y, Sheng S, Feng L, Zhang H, Zhang X. Plant 22-nt siRNAs mediate translational repression and stress adaptation. Nature. 2020;581:89\u201393.","journal-title":"Nature"},{"key":"4867_CR7","doi-asserted-by":"publisher","first-page":"44836","DOI":"10.1038\/srep44836","volume":"7","author":"F He","year":"2017","unstructured":"He F, Han Y, Gong J, Song J, Wang H, Li Y. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level. Sci Rep. 2017;7:44836.","journal-title":"Sci Rep"},{"issue":"3","key":"4867_CR8","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.jbi.2012.02.005","volume":"45","author":"M Mysara","year":"2012","unstructured":"Mysara M, Elhefnawi M, Garibaldi JM. MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (DeltaG). J Biomed Inform. 2012;45(3):528\u201334.","journal-title":"J Biomed Inform"},{"issue":"Suppl 7","key":"4867_CR9","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1186\/s12864-018-5028-8","volume":"19","author":"Y Han","year":"2018","unstructured":"Han Y, He F, Chen Y, Liu Y, Yu H. SiRNA silencing efficacy prediction based on a deep architecture. BMC Genomics. 2018;19(Suppl 7):669.","journal-title":"BMC Genomics"},{"key":"4867_CR10","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1186\/1479-5876-11-305","volume":"11","author":"A Qureshi","year":"2013","unstructured":"Qureshi A, Thakur N, Kumar M. VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses. J Transl Med. 2013;11:305.","journal-title":"J Transl Med"},{"issue":"3","key":"4867_CR11","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1038\/nbt936","volume":"22","author":"A Reynolds","year":"2004","unstructured":"Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, Khvorova A. Rational siRNA design for RNA interference. Nat Biotechnol. 2004;22(3):326\u201330.","journal-title":"Nat Biotechnol"},{"key":"4867_CR12","first-page":"201","volume":"361","author":"K Ui-Tei","year":"2007","unstructured":"Ui-Tei K, Naito Y, Saigo K. Guidelines for the selection of effective short-interfering RNA sequences for functional genomics. Methods Mol Biol. 2007;361:201\u201316.","journal-title":"Methods Mol Biol"},{"issue":"4","key":"4867_CR13","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.ygeno.2013.07.009","volume":"102","author":"L Liu","year":"2013","unstructured":"Liu L, Li QZ, Lin H, Zuo YC. The effect of regions flanking target site on siRNA potency. Genomics. 2013;102(4):215\u201322.","journal-title":"Genomics"},{"issue":"11","key":"4867_CR14","doi-asserted-by":"publisher","first-page":"e27602","DOI":"10.1371\/journal.pone.0027602","volume":"6","author":"WJ Pan","year":"2011","unstructured":"Pan WJ, Chen CW, Chu YW. siPRED: predicting siRNA efficacy using various characteristic methods. PLoS ONE. 2011;6(11):e27602.","journal-title":"PLoS ONE"},{"issue":"1","key":"4867_CR15","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1186\/s12859-018-2495-5","volume":"19","author":"X Jia","year":"2018","unstructured":"Jia X, Han Q, Lu Z. Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps. BMC Bioinform. 2018;19(1):512.","journal-title":"BMC Bioinform"},{"issue":"12","key":"4867_CR16","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1002\/2211-5463.12327","volume":"7","author":"X Jia","year":"2017","unstructured":"Jia X, Liu Y, Han Q, Lu Z. Multiple-cumulative probabilities used to cluster and visualize transcriptomes. FEBS Open Bio. 2017;7(12):2008\u201320.","journal-title":"FEBS Open Bio"},{"key":"4867_CR17","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/1471-2105-7-65","volume":"7","author":"SA Shabalina","year":"2006","unstructured":"Shabalina SA, Spiridonov AN, Ogurtsov AY. Computational models with thermodynamic and composition features improve siRNA design. BMC Bioinform. 2006;7:65.","journal-title":"BMC Bioinform"},{"key":"4867_CR18","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1186\/1471-2105-7-520","volume":"7","author":"JP Vert","year":"2006","unstructured":"Vert JP, Foveau N, Lajaunie C, Vandenbrouck Y. An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinform. 2006;7:520.","journal-title":"BMC Bioinform"},{"issue":"18","key":"4867_CR19","doi-asserted-by":"publisher","first-page":"e123","DOI":"10.1093\/nar\/gkm699","volume":"35","author":"M Ichihara","year":"2007","unstructured":"Ichihara M, Murakumo Y, Masuda A, Matsuura T, Asai N, Jijiwa M, Ishida M, Shinmi J, Yatsuya H, Qiao S, et al. Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities. Nucleic Acids Res. 2007;35(18):e123.","journal-title":"Nucleic Acids Res"},{"key":"4867_CR20","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1038\/nbt1118","volume":"23","author":"D Huesken","year":"2005","unstructured":"Huesken D, et al. Design of a genome-wide siRNA library using an artificial neural network. Nature Biotechnol. 2005;23:995\u20131001.","journal-title":"Nature Biotechnol"},{"key":"4867_CR21","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1186\/1471-2105-8-182","volume":"8","author":"AS Peek","year":"2007","unstructured":"Peek AS. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features. BMC Bioinform. 2007;8:182.","journal-title":"BMC Bioinform"},{"issue":"17","key":"4867_CR22","doi-asserted-by":"publisher","first-page":"7380","DOI":"10.1093\/nar\/gkr462","volume":"39","author":"N Bushati","year":"2011","unstructured":"Bushati N, Smith J, Briscoe J, Watkins C. An intuitive graphical visualization technique for the interrogation of transcriptome data. Nucleic Acids Res. 2011;39(17):7380\u20139.","journal-title":"Nucleic Acids Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04867-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04867-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04867-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T10:02:56Z","timestamp":1660384976000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04867-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,13]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4867"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04867-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2022,8,13]]},"assertion":[{"value":"8 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declared that they had no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"337"}}