{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:10:45Z","timestamp":1767964245179,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"S4","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Syst Biol"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1186\/s12918-016-0353-5","type":"journal-article","created":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T11:37:42Z","timestamp":1482493062000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":136,"title":["Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy"],"prefix":"10.1186","volume":"10","author":[{"given":"Quan","family":"Zou","sequence":"first","affiliation":[]},{"given":"Shixiang","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Ju","sequence":"additional","affiliation":[]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,12,23]]},"reference":[{"key":"353_CR1","doi-asserted-by":"crossref","first-page":"12955","DOI":"10.1073\/pnas.0704138104","volume":"104","author":"RD Kornberg","year":"2007","unstructured":"Kornberg RD. The molecular basis of eukaryotic transcription. Proc Natl Acad Sci. 2007;104:12955\u201361.","journal-title":"Proc Natl Acad Sci"},{"key":"353_CR2","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1186\/1471-2105-15-298","volume":"15","author":"L Song","year":"2014","unstructured":"Song L, et al. nDNA-prot: Identification of DNA-binding Proteins Based on Unbalanced Classification. BMC Bioinformatics. 2014;15:298.","journal-title":"BMC Bioinformatics"},{"key":"353_CR3","first-page":"686090","volume":"2013","author":"Q Zou","year":"2013","unstructured":"Zou Q, et al. An approach for identifying cytokines based on a novel ensemble classifier. Biomed Res Int. 2013;2013:686090.","journal-title":"Biomed Res Int"},{"key":"353_CR4","doi-asserted-by":"crossref","first-page":"e38979","DOI":"10.1371\/journal.pone.0038979","volume":"7","author":"X-Y Cheng","year":"2012","unstructured":"Cheng X-Y, et al. A global characterization and identification of multifunctional enzymes. PLoS One. 2012;7:e38979.","journal-title":"PLoS One"},{"key":"353_CR5","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ab.2012.03.015","volume":"425","author":"P Du","year":"2012","unstructured":"Du P, Wang X, Xu C, Gao Y. PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou\u2019s pseudo-amino acid compositions. Anal Biochem. 2012;425:117\u20139.","journal-title":"Anal Biochem"},{"key":"353_CR6","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jtbi.2015.08.025","volume":"385","author":"B Liu","year":"2015","unstructured":"Liu B, et al. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J Theor Biol. 2015;385:153\u20139.","journal-title":"J Theor Biol"},{"key":"353_CR7","doi-asserted-by":"crossref","first-page":"3692","DOI":"10.1093\/nar\/gkg600","volume":"31","author":"C Cai","year":"2003","unstructured":"Cai C, Han L, Ji ZL, Chen X, Chen YZ. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003;31:3692\u20137.","journal-title":"Nucleic Acids Res"},{"key":"353_CR8","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/TNB.2014.2352454","volume":"14","author":"L Wei","year":"2015","unstructured":"Wei L, Liao M, Gao X, Zou Q. An Improved Protein Structural Prediction Method by Incorporating Both Sequence and Structure Information. IEEE Trans Nanobioscience. 2015;14:339\u201349.","journal-title":"IEEE Trans Nanobioscience"},{"key":"353_CR9","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1109\/TNB.2015.2450233","volume":"14","author":"L Wei","year":"2015","unstructured":"Wei L, Liao M, Gao X, Zou Q. Enhanced Protein Fold Prediction Method through a Novel Feature Extraction Technique. IEEE Trans Nanobioscience. 2015;14:649\u201359.","journal-title":"IEEE Trans Nanobioscience"},{"key":"353_CR10","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1752-0509-9-S1-S10","volume":"9","author":"R Xu","year":"2015","unstructured":"Xu R, et al. Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation. BMC Syst Biol. 2015;9:S10.","journal-title":"BMC Syst Biol"},{"key":"353_CR11","doi-asserted-by":"crossref","first-page":"W65","DOI":"10.1093\/nar\/gkv458","volume":"43","author":"B Liu","year":"2015","unstructured":"Liu B, et al. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015;43:W65\u201371.","journal-title":"Nucleic Acids Res"},{"key":"353_CR12","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1093\/bioinformatics\/btv042","volume":"31","author":"N Xiao","year":"2015","unstructured":"Xiao N, Cao DS, Zhu MF, Xu QS. protr\/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics. 2015;31:1857\u20139.","journal-title":"Bioinformatics"},{"key":"353_CR13","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ab.2007.10.012","volume":"373","author":"H-B Shen","year":"2008","unstructured":"Shen H-B, Chou K-C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem. 2008;373:386\u20138.","journal-title":"Anal Biochem"},{"key":"353_CR14","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1186\/1471-2105-14-314","volume":"14","author":"Y Fang","year":"2013","unstructured":"Fang Y, Gao S, Tai D, Middaugh CR, Fang J. Identification of properties important to protein aggregation using feature selection. BMC Bioinformatics. 2013;14:314.","journal-title":"BMC Bioinformatics"},{"key":"353_CR15","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27:1226\u201338.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"353_CR16","first-page":"3221","volume":"15","author":"L Maaten Van Der","year":"2014","unstructured":"Van Der Maaten L. Accelerating t-sne using tree-based algorithms. J Mach Learn Res. 2014;15:3221\u201345.","journal-title":"J Mach Learn Res"},{"key":"353_CR17","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neucom.2014.12.123","volume":"173","author":"Q Zou","year":"2016","unstructured":"Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing. 2016;173:346\u201354.","journal-title":"Neurocomputing"},{"key":"353_CR18","doi-asserted-by":"crossref","first-page":"i221","DOI":"10.1093\/bioinformatics\/btv256","volume":"31","author":"H Liu","year":"2015","unstructured":"Liu H, Sun J, Guan J, Zheng J, Zhou S. Improving compound\u2013protein interaction prediction by building up highly credible negative samples. Bioinformatics. 2015;31:i221\u20139.","journal-title":"Bioinformatics"},{"key":"353_CR19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1093\/bib\/bbv033","volume":"17","author":"X Zeng","year":"2016","unstructured":"Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform. 2016;17:193\u2013203.","journal-title":"Brief Bioinform"},{"key":"353_CR20","first-page":"55","volume":"15","author":"Q Zou","year":"2016","unstructured":"Zou Q, Li J, Song L, Zeng X, Wang G. Similarity computation strategies in the microRNA-disease network: a survey. Brief Funct Genomics. 2016;15:55\u201364.","journal-title":"Brief Funct Genomics"},{"key":"353_CR21","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/TCBB.2013.146","volume":"11","author":"L Wei","year":"2014","unstructured":"Wei L, et al. Improved and promising identification of human microRNAs by incorporating a high-quality negative set. IEEE\/ACM Trans Comput Biol Bioinform. 2014;11:192\u2013201.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"353_CR22","doi-asserted-by":"crossref","first-page":"W538","DOI":"10.1093\/nar\/gkm254","volume":"35","author":"J-R Xu","year":"2007","unstructured":"Xu J-R, Zhang J-X, Han B-C, Liang L, Ji Z-L. CytoSVM: an advanced server for identification of cytokine-receptor interactions. Nucleic Acids Res. 2007;35:W538\u201342.","journal-title":"Nucleic Acids Res"},{"key":"353_CR23","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1162\/NECO_a_00605","volume":"26","author":"X Zeng","year":"2014","unstructured":"Zeng X, Zhang X, Song T, Pan L. Spiking Neural P Systems with Thresholds. Neural Comput. 2014;26:1340\u201361.","journal-title":"Neural Comput"},{"key":"353_CR24","doi-asserted-by":"crossref","first-page":"289","DOI":"10.2174\/157016461104150121115154","volume":"11","author":"X Zhao","year":"2014","unstructured":"Zhao X, Zou Q, Liu B, Liu X. Exploratory predicting protein folding model with random forest and hybrid features. Curr Proteomics. 2014;11:289\u201399.","journal-title":"Curr Proteomics"},{"key":"353_CR25","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1002\/minf.201500031","volume":"34","author":"Q Zou","year":"2015","unstructured":"Zou Q, et al. Improving tRNAscan-SE annotation results via ensemble classifiers. Mol Inf. 2015;34:761\u201370.","journal-title":"Mol Inf"},{"key":"353_CR26","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4238\/2015.January.15.15","volume":"14","author":"C Wang","year":"2015","unstructured":"Wang C, Hu L, Guo M, Liu X, Zou Q. imDC: an ensemble learning method for imbalanced classification with miRNA data. Genet Mol Res. 2015;14:123\u201333.","journal-title":"Genet Mol Res"},{"key":"353_CR27","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1093\/bioinformatics\/btt709","volume":"30","author":"B Liu","year":"2014","unstructured":"Liu B, et al. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics. 2014;30:472\u20139.","journal-title":"Bioinformatics"},{"key":"353_CR28","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1093\/bioinformatics\/17.4.349","volume":"17","author":"C Ding","year":"2001","unstructured":"Ding C, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics. 2001;17:349\u201358.","journal-title":"Bioinformatics"},{"key":"353_CR29","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1002\/prot.20605","volume":"62","author":"HH Lin","year":"2006","unstructured":"Lin HH, Han LY, Cai CZ, Ji ZL, Chen YZ. Prediction of transporter family from protein sequence by support vector machine approach. Proteins: Struct, Funct, Bioinf. 2006;62:218\u201331.","journal-title":"Proteins: Struct, Funct, Bioinf"},{"issue":"8","key":"353_CR30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.2174\/1570164611310010002","volume":"888","author":"Q Zou","year":"2013","unstructured":"Zou Q, Li X, Jiang Y, Zhao Y, Wang G. BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types. Curr Proteomics. 2013;888(8):2\u20139.","journal-title":"Curr Proteomics"},{"key":"353_CR31","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1006\/jmbi.1999.3091","volume":"292","author":"DT Jones","year":"1999","unstructured":"Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol. 1999;292:195\u2013202.","journal-title":"J Mol Biol"},{"key":"353_CR32","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","volume":"25","author":"SF Altschul","year":"1997","unstructured":"Altschul SF, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389\u2013402.","journal-title":"Nucleic Acids Res"},{"key":"353_CR33","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1093\/bioinformatics\/btv413","volume":"31","author":"B Liu","year":"2015","unstructured":"Liu B, Chen J, Wang X. Application of Learning to Rank to protein remote homology detection. Bioinformatics. 2015;31:3492\u20138.","journal-title":"Bioinformatics"},{"key":"353_CR34","doi-asserted-by":"crossref","first-page":"D190","DOI":"10.1093\/nar\/gkm895","volume":"36","author":"U Consortium","year":"2008","unstructured":"Consortium U. The universal protein resource (UniProt). Nucleic Acids Res. 2008;36:D190\u20135.","journal-title":"Nucleic Acids Res"},{"key":"353_CR35","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1093\/bioinformatics\/btq003","volume":"26","author":"Y Huang","year":"2010","unstructured":"Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26:680\u20132.","journal-title":"Bioinformatics"},{"key":"353_CR36","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.medengphy.2014.07.008","volume":"36","author":"S Yang","year":"2014","unstructured":"Yang S, et al. Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method. Med Eng Phys. 2014;36:1305\u201311. doi: 10.1016\/j.medengphy.2014.07.008 .","journal-title":"Med Eng Phys"},{"key":"353_CR37","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1093\/bioinformatics\/btp500","volume":"25","author":"Q Dong","year":"2009","unstructured":"Dong Q, Zhou S, Guan J. A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics. 2009;25:2655\u201362.","journal-title":"Bioinformatics"},{"key":"353_CR38","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/0952813X.2010.506288","volume":"23","author":"Y Wu","year":"2011","unstructured":"Wu Y, Krishnan S. Combining least-squares support vector machines for classification of biomedical signals: a case study with knee-joint vibroarthrographic signals. J Exp Theor Artif Intell. 2011;23:63\u201377. doi: 10.1080\/0952813X.2010.506288 .","journal-title":"J Exp Theor Artif Intell"},{"key":"353_CR39","doi-asserted-by":"crossref","first-page":"23934","DOI":"10.1038\/srep23934","volume":"6","author":"R Wang","year":"2016","unstructured":"Wang R, Xu Y, Liu B. Recombination spot identification Based on gapped k-mers. Sci Rep. 2016;6:23934.","journal-title":"Sci Rep"},{"key":"353_CR40","doi-asserted-by":"crossref","first-page":"19062","DOI":"10.1038\/srep19062","volume":"6","author":"J Chen","year":"2016","unstructured":"Chen J, Wang X, Liu B. iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions. Sci Rep. 2016;6:19062.","journal-title":"Sci Rep"},{"key":"353_CR41","first-page":"389","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ. LIBSVM: A library for support vector machines. ACM Trans Intell SystTechnol. 2011;2:389\u201396.","journal-title":"ACM Trans Intell SystTechnol"},{"key":"353_CR42","doi-asserted-by":"crossref","first-page":"e106691","DOI":"10.1371\/journal.pone.0106691","volume":"9","author":"B Liu","year":"2014","unstructured":"Liu B, et al. iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition. PLoS One. 2014;9:e106691.","journal-title":"PLoS One"},{"key":"353_CR43","doi-asserted-by":"publisher","unstructured":"Wu, Y. et al. Adaptive linear and normalized combination of radial basis function networks for function approximation and regression. Math Probl Eng. 2014, {Article ID} 913897, doi: 10.1155\/2014\/913897 .","DOI":"10.1155\/2014\/913897"},{"key":"353_CR44","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.3390\/e15041375","volume":"15","author":"Y Wu","year":"2013","unstructured":"Wu Y, Cai S, Yang S, Zheng F, Xiang N. Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability decision criterion. Entropy. 2013;15:1375\u201387. doi: 10.3390\/e15041375 .","journal-title":"Entropy"},{"key":"353_CR45","doi-asserted-by":"publisher","first-page":"e88825","DOI":"10.1371\/journal.pone.0088825","volume":"9","author":"S Yang","year":"2014","unstructured":"Yang S, et al. Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with {Parkinson\u2019s} disease. PLoS One. 2014;9:e88825. doi: 10.1371\/journal.pone.0088825 .","journal-title":"PLoS One"},{"key":"353_CR46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, et al. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsl. 2009;11:10\u20138.","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"353_CR47","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.ins.2014.03.053","volume":"278","author":"X Zhang","year":"2014","unstructured":"Zhang X, Liu Y, Luo B, Pan L. Computational power of tissue P systems for generating control languages. Inf Sci. 2014;278:285\u201397.","journal-title":"Inf Sci"},{"key":"353_CR48","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/02331888.2010.500735","volume":"46","author":"NS Altman","year":"2012","unstructured":"Altman NS. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am Stat. 2012;46:175\u201385.","journal-title":"Am Stat"},{"key":"353_CR49","doi-asserted-by":"crossref","unstructured":"Ho TK. Random decision forests. In Document Analysis and Recognition. Proceedings of the Third International Conference on (Vol. 1, pp. 278-282). IEEE; 1995.","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"353_CR50","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK. The Random Subspace Method for Constructing Decision Forests. IEEE Trans Pattern Anal Mach Intell. 1998;20:832\u201344.","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["BMC Systems Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12918-016-0353-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T20:45:36Z","timestamp":1749847536000},"score":1,"resource":{"primary":{"URL":"http:\/\/bmcsystbiol.biomedcentral.com\/articles\/10.1186\/s12918-016-0353-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12]]},"references-count":50,"journal-issue":{"issue":"S4","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["353"],"URL":"https:\/\/doi.org\/10.1186\/s12918-016-0353-5","relation":{},"ISSN":["1752-0509"],"issn-type":[{"value":"1752-0509","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12]]},"article-number":"114"}}