{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T13:14:33Z","timestamp":1672060473972},"reference-count":51,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2016,2]]},"abstract":"<jats:p> MicroRNAs (miRNAs) are a set of short (21\u201324 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. Therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). These features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). The results show that the specificity ([Formula: see text])and sensitivity ([Formula: see text]) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time. <\/jats:p>","DOI":"10.1142\/s0219720016500062","type":"journal-article","created":{"date-parts":[[2015,10,27]],"date-time":"2015-10-27T07:07:52Z","timestamp":1445929672000},"page":"1650006","source":"Crossref","is-referenced-by-count":1,"title":["OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features"],"prefix":"10.1142","volume":"14","author":[{"given":"Cong","family":"Pian","sequence":"first","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Yuan-Yuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Qin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]},{"given":"Liang-Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2016,2,24]]},"reference":[{"key":"S0219720016500062BIB001","doi-asserted-by":"publisher","DOI":"10.1038\/sj.emboj.7600385"},{"key":"S0219720016500062BIB002","unstructured":"K Appasani et al.,  MicroRNAs: From Basic Science to Disease Biology. (Cambridge University Press,  2008), pp. 7\u201321."},{"key":"S0219720016500062BIB003","doi-asserted-by":"publisher","DOI":"10.1261\/rna.2146906"},{"key":"S0219720016500062BIB004","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0403115101"},{"key":"S0219720016500062BIB005","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0405570102"},{"key":"S0219720016500062BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/S0960-9822(03)00281-1"},{"key":"S0219720016500062BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2009.01.046"},{"key":"S0219720016500062BIB008","doi-asserted-by":"publisher","DOI":"10.2741\/2234"},{"key":"S0219720016500062BIB009","doi-asserted-by":"publisher","DOI":"10.1111\/j.1432-0436.2004.07202003.x"},{"key":"S0219720016500062BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2012.02.001"},{"key":"S0219720016500062BIB011","doi-asserted-by":"publisher","DOI":"10.1101\/gad.1074403"},{"key":"S0219720016500062BIB012","doi-asserted-by":"publisher","DOI":"10.1126\/science.1080372"},{"key":"S0219720016500062BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/S1097-2765(03)00153-9"},{"key":"S0219720016500062BIB014","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2003-4-7-r42"},{"key":"S0219720016500062BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.molcel.2004.05.027"},{"key":"S0219720016500062BIB016","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0404025101"},{"key":"S0219720016500062BIB017","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-6-310"},{"key":"S0219720016500062BIB018","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm026"},{"key":"S0219720016500062BIB019","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-6-267"},{"key":"S0219720016500062BIB020","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth746"},{"key":"S0219720016500062BIB021","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gki668"},{"key":"S0219720016500062BIB022","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl094"},{"key":"S0219720016500062BIB023","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkm368"},{"key":"S0219720016500062BIB024","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr247"},{"key":"S0219720016500062BIB025","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2010-11-4-r39"},{"key":"S0219720016500062BIB026","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gks1187"},{"key":"S0219720016500062BIB027","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq329"},{"key":"S0219720016500062BIB028","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1515\/jib-2013-215","volume":"10","author":"Sacar MD","year":"2013","journal-title":"J Integr Bioinform"},{"key":"S0219720016500062BIB029","first-page":"e138","author":"Peace RJ","year":"2015","journal-title":"Nucl Acids Res"},{"key":"S0219720016500062BIB030","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-9-185"},{"key":"S0219720016500062BIB031","first-page":"D68","volume":"42","author":"Ana K","year":"2013","journal-title":"Nucl Acids Res"},{"key":"S0219720016500062BIB032","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkg599"},{"key":"S0219720016500062BIB033","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth466"},{"key":"S0219720016500062BIB034","doi-asserted-by":"publisher","DOI":"10.4236\/jbise.2013.64054"},{"key":"S0219720016500062BIB035","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt072"},{"key":"S0219720016500062BIB036","doi-asserted-by":"publisher","DOI":"10.3390\/ijms15033495"},{"key":"S0219720016500062BIB037","doi-asserted-by":"publisher","DOI":"10.3390\/ijms15021746"},{"key":"S0219720016500062BIB039","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"S0219720016500062BIB040","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"S0219720016500062BIB041","volume-title":"Statistical Learning Theory","author":"Vapnik V","year":"1998"},{"key":"S0219720016500062BIB043","doi-asserted-by":"publisher","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"S0219720016500062BIB044","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2010.12.024"},{"key":"S0219720016500062BIB045","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0106691"},{"key":"S0219720016500062BIB046","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0105018"},{"key":"S0219720016500062BIB047","doi-asserted-by":"publisher","DOI":"10.1080\/1062936X.2013.773378"},{"key":"S0219720016500062BIB048","doi-asserted-by":"publisher","DOI":"10.2174\/157340613804488341"},{"key":"S0219720016500062BIB049","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2014.04.006"},{"key":"S0219720016500062BIB050","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2014.06.007"},{"key":"S0219720016500062BIB051","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2013.08.037"},{"key":"S0219720016500062BIB052","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2014.07.003"},{"key":"S0219720016500062BIB053","doi-asserted-by":"publisher","DOI":"10.1016\/j.ab.2014.06.022"}],"container-title":["Journal of Bioinformatics and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219720016500062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T19:27:12Z","timestamp":1565119632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219720016500062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2]]},"references-count":51,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2016,2,24]]},"published-print":{"date-parts":[[2016,2]]}},"alternative-id":["10.1142\/S0219720016500062"],"URL":"https:\/\/doi.org\/10.1142\/s0219720016500062","relation":{},"ISSN":["0219-7200","1757-6334"],"issn-type":[{"value":"0219-7200","type":"print"},{"value":"1757-6334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,2]]}}}