{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T09:03:59Z","timestamp":1778576639605,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T00:00:00Z","timestamp":1534464000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"ational Natural Science Foundation of China","award":["31260203"],"award-info":[{"award-number":["31260203"]}]},{"name":"ational Natural Science Foundation of China","award":["51467015"],"award-info":[{"award-number":["51467015"]}]},{"name":"Natural Science Foundation of the Inner Mongolia of China","award":["2016MS0378"],"award-info":[{"award-number":["2016MS0378"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s11227-018-2531-2","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T14:10:48Z","timestamp":1534515048000},"page":"3199-3210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using random forest algorithm to predict super-secondary structure in proteins"],"prefix":"10.1007","volume":"76","author":[{"given":"Xiu-zhen","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai-xia","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-jiang","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su-juan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,17]]},"reference":[{"issue":"8","key":"2531_CR1","doi-asserted-by":"publisher","first-page":"e0183756","DOI":"10.1371\/journal.pone.0183756","volume":"12","author":"XY Cao","year":"2017","unstructured":"Cao XY, Hu XZ, Zhang XJ, Gao SJ, Ding CJ, Feng YE, Bao WH (2017) Identification of metal ion binding sites based on amino acid sequences. PLoS ONE 12(8):e0183756","journal-title":"PLoS ONE"},{"issue":"34","key":"2531_CR2","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1093\/nar\/gkl071","volume":"34","author":"L Conde","year":"2006","unstructured":"Conde L, Vaquerizas JM, Dopazo H, Arbiza L, Reumers J, Rousseau F et al (2006) PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes. Nucleic Acids Res 34(34):621\u2013625","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"2531_CR3","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1002\/humu.21573","volume":"32","author":"R Levy","year":"2011","unstructured":"Levy R, Sobolev V, Edelman M (2011) First and second shell metal binding residues in human proteins are disproportionately associated with disease related SNPs. Hum Mutat 32(11):1309\u20131318","journal-title":"Hum Mutat"},{"key":"2531_CR4","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/0014-5793(95)00166-7","volume":"361","author":"R Gurunath","year":"1995","unstructured":"Gurunath R, Beena TK, Adiga PR, Balaram P (1995) Enhancing peptide antigenicity by helix stabilization. FEBS Lett 361:176\u2013178","journal-title":"FEBS Lett"},{"key":"2531_CR5","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1093\/protein\/10.7.763","volume":"10","author":"ZR Sun","year":"1997","unstructured":"Sun ZR, Rao XQ, Peng LW, Xu D (1997) Prediction of protein supersecondary structures based on the artificial neural network method. Protein Eng 10:763\u2013769","journal-title":"Protein Eng"},{"issue":"6","key":"2531_CR6","first-page":"424","volume":"13","author":"XZ Hu","year":"2006","unstructured":"Hu XZ, Li QZ (2006) The protein super-secondary structure recognition with the method of diversity measure. Acta Biophysica Sinica 13(6):424\u2013428","journal-title":"Acta Biophysica Sinica"},{"key":"2531_CR7","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10930-007-9114-z","volume":"27","author":"XZ Hu","year":"2008","unstructured":"Hu XZ, Li QZ (2008) Prediction of the \u03b2-Hairpins in protein using support vector machine. Protein J 27:115\u2013122","journal-title":"Protein J"},{"key":"2531_CR8","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1002\/jcc.21616","volume":"32","author":"DH Zou","year":"2011","unstructured":"Zou DH, He ZS, He JY, Xia YX (2011) Supersecondary structure prediction using chou\u2019s pseudo amino acid composition. J Comput Chem 32:271\u2013278","journal-title":"J Comput Chem"},{"key":"2531_CR9","first-page":"1","volume":"146","author":"WZ Mao","year":"2018","unstructured":"Mao WZ, Wang T, Zhang W, Gong H (2018) Identification of residue pairing in interacting \u03b2-strands from a predicted residue contact map. BMC Bioinform 146:1\u201319","journal-title":"BMC Bioinform"},{"key":"2531_CR10","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1002\/bip.360221211","volume":"22","author":"W Kabsch","year":"1983","unstructured":"Kabsch W, Sander C (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen- bonded and geometrical features. Biopolymers 22:2577\u20132637","journal-title":"Biopolymers"},{"key":"2531_CR11","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1006\/jmbi.1996.0819","volume":"266","author":"B Oliva","year":"1997","unstructured":"Oliva B, Bates PA, Querol E, Avil\u00e9s FX, Sternberg MJ (1997) An automated classification of the StrucT of protein loops. J Mol Biol 266:814\u2013830","journal-title":"J Mol Biol"},{"key":"2531_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1093\/nar\/gkh002","volume":"32","author":"J Espadaler","year":"2004","unstructured":"Espadaler J, Fuentes NF, Hermoso A, Querol E, Aviles FX, Sternberg MJE, Oliva B (2004) ArchDB: automated protein loop classification as a tool for structural genomics. Nucleic Acids Res. 32:185\u2013188","journal-title":"Nucleic Acids Res."},{"key":"2531_CR13","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1093\/nar\/gkt840","volume":"42","author":"B Jaume","year":"2014","unstructured":"Jaume B, Joan PI, Javier GG, Manuel A, Narcis ML, Fuentes F, Oliva B (2014) ArchDB 2014: structural classification of loops in proteins. Nucleic Acids Res 42:315\u2013319","journal-title":"Nucleic Acids Res"},{"key":"2531_CR14","unstructured":"Leo B (2001) Random Forest. Statistics. Department University of California Berkeley, CA, vol 94720, pp 1\u20132"},{"issue":"2","key":"2531_CR15","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1039\/C4MB00569D","volume":"11","author":"C Li","year":"2015","unstructured":"Li C, Wang XF, Chen Z, Zhang ZD, Song JN (2015) Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices. Mol Biosyst 11(2):354\u2013360","journal-title":"Mol Biosyst"},{"key":"2531_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.jtbi.2018.01.023","volume":"443","author":"JN Song","year":"2018","unstructured":"Song JN, Li FY, Takemoto K, Haffari G, Akutsu T, Chou KC, Webb GI (2018) PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J Theor Biol 443:125\u2013137","journal-title":"J Theor Biol"},{"key":"2531_CR17","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-540-72849-8_61","volume":"4478","author":"O Okun","year":"2007","unstructured":"Okun O, Priisalu H (2007) Random forest for gene expression based cancer classification: overlooked issues. Pattern Recognit Image Anal 4478:483\u2013490","journal-title":"Pattern Recognit Image Anal"},{"issue":"6","key":"2531_CR18","doi-asserted-by":"publisher","first-page":"609","DOI":"10.2174\/092986611795222777","volume":"18","author":"SC Jia","year":"2011","unstructured":"Jia SC, Hu XZ (2011) Using random forest algorithm to predict \u03b2-hairpin motifs. Protein Pept Lett 18(6):609\u2013617","journal-title":"Protein Pept Lett"},{"issue":"2","key":"2531_CR19","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s10822-016-9999-8","volume":"31","author":"T Richa","year":"2017","unstructured":"Richa T, Ide S, Suzuki R, Ebina T, Kuroda Y (2017) Fast H-DROP: a thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. J Comput Aided Mol Des 31(2):237\u2013244","journal-title":"J Comput Aided Mol Des"},{"issue":"13","key":"2531_CR20","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1093\/bioinformatics\/btq253","volume":"26","author":"ZP Liu","year":"2010","unstructured":"Liu ZP, Wu LY, Wang Y, Zhang XS, Chen LN (2010) Prediction of protein\u2013RNA binding sites by a random forest method with combined features. Bioinformatics 26(13):1616\u20131622","journal-title":"Bioinformatics"},{"key":"2531_CR21","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1002\/prot.20356","volume":"58","author":"J P\u00e1nek","year":"2005","unstructured":"P\u00e1nek J, Eidhammer I, Aasland R (2005) A new method for identification of protein (sub) families in a set of proteins based on hydropathy distribution in proteins. Proteins Struct Funct Bioinform 58:923\u2013934","journal-title":"Proteins Struct Funct Bioinform"},{"issue":"13","key":"2531_CR22","doi-asserted-by":"publisher","first-page":"3576","DOI":"10.1093\/nar\/gkg585","volume":"31","author":"AE Kel","year":"2003","unstructured":"Kel AE, GoBling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E (2003) MATCHTM: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acid Res 31(13):3576\u20133579","journal-title":"Nucleic Acid Res"},{"key":"2531_CR23","doi-asserted-by":"publisher","first-page":"4878","DOI":"10.1093\/nar\/23.23.4878","volume":"23","author":"K Quandt","year":"1995","unstructured":"Quandt K, Frech K, Karas H, Wingender E, Werner T (1995) MatInd and MatInspector: new fast and versatile tools for detection of consensus matches in nucleotide sequence data. Nucleic Acids Res 23:4878\u20134884","journal-title":"Nucleic Acids Res"},{"key":"2531_CR24","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1093\/bioinformatics\/bti473","volume":"13","author":"K Cartharius","year":"2005","unstructured":"Cartharius K, Frech K, Grote K, Klocke B, Haltmeier M, Klingenhoff A, Frisch M, Bayerlein M, Werner T (2005) MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics 13:2933\u20132942","journal-title":"Bioinformatics"},{"key":"2531_CR25","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1093\/nar\/gki588","volume":"33","author":"M Kumar","year":"2005","unstructured":"Kumar M, Bhasin M, Natt NK, Raghava GPS (2005) BhairPred: prediction of b-hairpins in a protein from multiple alignment information using ANN and SVM techniques. Nucleic Acids Res 33:154\u2013155","journal-title":"Nucleic Acids Res"},{"key":"2531_CR26","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1002\/prot.10589","volume":"54","author":"M Kuhn","year":"2004","unstructured":"Kuhn M, Meiler J, Baker D (2004) Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins. Proteins Struct Funct Bioinform 54:282\u2013288","journal-title":"Proteins Struct Funct Bioinform"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-018-2531-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-018-2531-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-018-2531-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,10]],"date-time":"2020-05-10T08:07:48Z","timestamp":1589098068000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-018-2531-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,17]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["2531"],"URL":"https:\/\/doi.org\/10.1007\/s11227-018-2531-2","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,17]]},"assertion":[{"value":"17 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}