{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T04:29:46Z","timestamp":1770956986435,"version":"3.50.1"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100010697","name":"Institute for Chemical Research, Kyoto University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010697","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI111965"],"award-info":[{"award-number":["R01 AI111965"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP120104460"],"award-info":[{"award-number":["DP120104460"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["LP110200333"],"award-info":[{"award-number":["LP110200333"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["1092262"],"award-info":[{"award-number":["1092262"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing \u2018Black-box\u2019 approaches that are unable to reveal causal relationships from large amounts of initially encoded features.<\/jats:p>","DOI":"10.1093\/bib\/bbaa049","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T12:35:43Z","timestamp":1584016543000},"page":"2126-2140","source":"Crossref","is-referenced-by-count":70,"title":["Computational prediction and interpretation of both general and specific types of promoters in <i>Escherichia coli<\/i> by exploiting a stacked ensemble-learning framework"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5216-3213","authenticated-orcid":false,"given":"Fuyi","family":"Li","sequence":"first","affiliation":[{"name":"Northwest A&F University, China"},{"name":"Department of Biochemistry and Molecular Biology and the Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University from the College of Information Engineering, Northwest A&F University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[{"name":"Monash University and also serves as a Deep Learning Specialist at NVIDIA AI Technology Centre. Before joining Monash, he was a research scientist at IBM Research Australia doing research in medical AI during 2016\u20132018. His research interests are AI, computer vision, medical image, robotics and deep learning"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya","family":"Wen","sequence":"additional","affiliation":[{"name":"computer technology from Ningxia University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanwei","family":"Yue","sequence":"additional","affiliation":[{"name":"medical science from Southern Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morihiro","family":"Hayashida","sequence":"additional","affiliation":[{"name":"informatics from Kyoto University, Japan, in 2005. He is an Assistant Professor in the Department of Electrical Engineering and Computer Science, National Institute of Technology, Matsue College, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelkader","family":"Baggag","sequence":"additional","affiliation":[{"name":"computer science from the University of Minnesota. He is a Senior Scientist at the Qatar Computing Research Institute (QCRI) and has a joint appointment as an Associate Professor at Hamad Bin Khalifa University (HBKU) in the Division of Information and Computing Technology. His research interests include data mining, linear algebra and machine learning"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Halima","family":"Bensmail","sequence":"additional","affiliation":[{"name":"University of Pierre & Marie Currie (Paris 6) in France. She is currently a Principal Scientist at QCRI-HBKU and a joint Associate Professor at the College of Computer and Science Engineering, HBKU"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute, Monash University, Australia. He is also affiliated with the Monash Centre for Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining, and pattern recognition"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"2021070817465204300_ref1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.bpc.2008.09.007","article-title":"Energetic contributions to the initiation of transcription in E. coli","volume":"138","author":"Ramprakash","year":"2008","journal-title":"Biophys Chem"},{"key":"2021070817465204300_ref2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1146\/annurev-micro-092412-155737","article-title":"Bacterial sigma factors: a historical, structural, and genomic perspective","volume":"68","author":"Feklistov","year":"2014","journal-title":"Annu Rev Microbiol"},{"key":"2021070817465204300_ref3","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.1093\/nar\/27.22.4305","article-title":"Compilation and analysis of sigma(54)-dependent promoter sequences","volume":"27","author":"Barrios","year":"1999","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref4","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.resmic.2007.09.001","article-title":"Structure and evolution of gene regulatory networks in microbial genomes","volume":"158","author":"Janga","year":"2007","journal-title":"Res Microbiol"},{"key":"2021070817465204300_ref5","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1111\/j.1574-6976.2007.00092.x","article-title":"Sigma factors in pseudomonas aeruginosa","volume":"32","author":"Potvin","year":"2008","journal-title":"FEMS Microbiol Rev"},{"key":"2021070817465204300_ref6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/bioinformatics\/btx579","article-title":"iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC","volume":"34","author":"Liu","year":"2018","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref7","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1093\/nar\/11.8.2237","article-title":"Compilation and analysis of Escherichia coli promoter DNA sequences","volume":"11","author":"Hawley","year":"1983","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref8","doi-asserted-by":"crossref","first-page":"5574","DOI":"10.1128\/jb.179.17.5574-5581.1997","article-title":"A transcriptional activator, FleQ, regulates mucin adhesion and flagellar gene expression in Pseudomonas aeruginosa in a cascade manner","volume":"179","author":"Arora","year":"1997","journal-title":"J Bacteriol"},{"key":"2021070817465204300_ref9","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.jtbi.2006.02.007","article-title":"The recognition and prediction of sigma70 promoters in Escherichia coli K-12","volume":"242","author":"Li","year":"2006","journal-title":"J Theor Biol"},{"key":"2021070817465204300_ref10","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1093\/nar\/gkr795","article-title":"Recognition of prokaryotic promoters based on a novel variable-window Z-curve method","volume":"40","author":"Song","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref11","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1006\/jtbi.1997.0401","article-title":"A symmetrical theory of DNA sequences and its applications","volume":"187","author":"Zhang","year":"1997","journal-title":"J Theor Biol"},{"key":"2021070817465204300_ref12","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1093\/bioinformatics\/btg041","article-title":"The Z curve database: a graphic representation of genome sequences","volume":"19","author":"Zhang","year":"2003","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.biologicals.2013.10.001","article-title":"DNA duplex stability as discriminative characteristic for Escherichia coli \u03c354- and \u03c328- dependent promoter sequences","volume":"42","author":"de Avilae Silva","year":"2014","journal-title":"Biologicals"},{"key":"2021070817465204300_ref14","doi-asserted-by":"crossref","first-page":"12961","DOI":"10.1093\/nar\/gku1019","article-title":"iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition","volume":"42","author":"Lin","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref15","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1093\/bioinformatics\/btz016","article-title":"MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters","volume":"35","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref16","doi-asserted-by":"crossref","first-page":"D212","DOI":"10.1093\/nar\/gky1077","article-title":"RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12","volume":"47","author":"Santos-Zavaleta","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref17","doi-asserted-by":"crossref","first-page":"D133","DOI":"10.1093\/nar\/gkv1156","article-title":"RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond","volume":"44","author":"Gama-Castro","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref18","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1093\/bioinformatics\/btl151","article-title":"Two sample logo: a graphical representation of the differences between two sets of sequence alignments","volume":"22","author":"Vacic","year":"2006","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref19","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz041","article-title":"iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data","author":"Chen","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref20","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1093\/bib\/bbx165","article-title":"BioSeq-analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches","volume":"20","author":"Liu","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref21","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.1093\/nar\/gkz740","article-title":"BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref22","doi-asserted-by":"crossref","first-page":"W65","DOI":"10.1093\/nar\/gkv458","article-title":"Pse-in-one: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref23","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/bioinformatics\/btu602","article-title":"PseKNC-general: a cross-platform package for generating various modes of pseudo nucleotide compositions","volume":"31","author":"Chen","year":"2015","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref24","doi-asserted-by":"crossref","first-page":"D37","DOI":"10.1093\/nar\/gkn597","article-title":"DiProDB: a database for dinucleotide properties","volume":"37","author":"Friedel","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2021070817465204300_ref25","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/bioinformatics\/bty653","article-title":"StackDPPred: a stacking based prediction of DNA-binding protein from sequence","volume":"35","author":"Mishra","year":"2019","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref26","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz022","article-title":"Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction","author":"Su","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref27","first-page":"155","author":"Verma","year":"2017"},{"key":"2021070817465204300_ref28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2021070817465204300_ref29","author":"Freund"},{"key":"2021070817465204300_ref30","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann Stat"},{"key":"2021070817465204300_ref31","first-page":"1","author":"Chen","year":"2015"},{"key":"2021070817465204300_ref32","first-page":"3146","volume-title":"Advances in Neural Information Processing Systems (NIPS)","author":"Ke","year":"2017"},{"key":"2021070817465204300_ref33","doi-asserted-by":"crossref","first-page":"34595","DOI":"10.1038\/srep34595","article-title":"GlycoMine(struct): a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features","volume":"6","author":"Li","year":"2016","journal-title":"Sci Rep"},{"key":"2021070817465204300_ref34","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1093\/bioinformatics\/btu852","article-title":"GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome","volume":"31","author":"Li","year":"2015","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref35","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1093\/bioinformatics\/btw186","article-title":"iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework","volume":"32","author":"Liu","year":"2016","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref36","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz061","article-title":"A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies","author":"Tang","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref37","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz048","article-title":"Evaluation of different computational methods on 5-methylcytosine sites identification","author":"Lv","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref38","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1021\/acs.jproteome.9b00074","article-title":"Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties","volume":"18","author":"Lian","year":"2019","journal-title":"J Proteome Res"},{"key":"2021070817465204300_ref39","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1023\/A:1013912006537","article-title":"Logistic regression, AdaBoost and Bregman distances","volume":"48","author":"Collins","year":"2002","journal-title":"Mach Learn"},{"key":"2021070817465204300_ref40","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1093\/bioinformatics\/btl482","article-title":"PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm","volume":"22","author":"Xie","year":"2006","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref41","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1093\/bioinformatics\/bty908","article-title":"BiXGBoost: a scalable, flexible boosting-based method for reconstructing gene regulatory networks","volume":"35","author":"Zheng","year":"2019","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref42","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.chemolab.2019.06.003","article-title":"LightGBM-PPI: predicting protein-protein interactions through LightGBM with multi-information fusion","volume":"191","author":"Chen","year":"2019","journal-title":"Chemom Intel Lab Syst"},{"key":"2021070817465204300_ref43","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/s12859-018-2527-1","article-title":"PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine","volume":"19","author":"Deng","year":"2018","journal-title":"Bmc Bioinformatics"},{"key":"2021070817465204300_ref44","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1002\/prot.25801","article-title":"Boosting phosphorylation site prediction with sequence feature-based machine learning","volume":"88","author":"Maiti","year":"2020","journal-title":"Proteins"},{"key":"2021070817465204300_ref45","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.1093\/bioinformatics\/bty1043","article-title":"PredGly: predicting lysine glycation sites for Homo sapiens based on XGboost feature optimization","volume":"35","author":"Yu","year":"2019","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref46","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"2021070817465204300_ref47","doi-asserted-by":"crossref","first-page":"638","DOI":"10.21105\/joss.00638","article-title":"MLxtend: providing machine learning and data science utilities and extensions to Python's scientific computing stack","volume":"3","author":"Raschka","year":"2018","journal-title":"J Open Source Software"},{"key":"2021070817465204300_ref48","doi-asserted-by":"crossref","DOI":"10.1201\/b17320","volume-title":"Data classification: algorithms and applications","author":"Aggarwal","year":"2014"},{"key":"2021070817465204300_ref49","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1093\/bioinformatics\/btz721","article-title":"DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites","volume":"36","author":"Li","year":"2020","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref50","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s12859-019-2700-1","article-title":"Positive-unlabelled learning of glycosylation sites in the human proteome","volume":"20","author":"Li","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2021070817465204300_ref51","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz051","article-title":"A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction","author":"Mei","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref52","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1093\/bioinformatics\/btx670","article-title":"PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy","volume":"34","author":"Song","year":"2018","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref53","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz120","article-title":"Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery","author":"Hong","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref54","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz081","article-title":"Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning","author":"Hong","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref55","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1093\/bib\/bbu031","article-title":"Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features","volume":"16","author":"Chen","year":"2015","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref56","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1093\/bib\/bbx123","article-title":"Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods","volume":"20","author":"Yang","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref57","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz123","article-title":"A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae","author":"Yang","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref58","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1093\/bioinformatics\/bty522","article-title":"Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome","volume":"34","author":"Li","year":"2018","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref59","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1093\/bib\/bby077","article-title":"Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods","volume":"20","author":"Li","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref60","first-page":"2951","volume-title":"Advances in Neural Information Processing Systems (NIPS)","author":"Snoek","year":"2012"},{"key":"2021070817465204300_ref61","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/BF00117832","article-title":"Stacked regressions","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach Learn"},{"key":"2021070817465204300_ref62","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw"},{"key":"2021070817465204300_ref63","first-page":"4765","volume-title":"Advances in neural information processing systems (NIPS)","author":"Lundberg","year":"2017"},{"key":"2021070817465204300_ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2957758","article-title":"Formator: predicting lysine formylation sites based on the most distant undersampling and safe-level synthetic minority oversampling","author":"Jia","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2021070817465204300_ref65","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1186\/s12859-019-3178-6","article-title":"SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models","volume":"20","author":"Wang","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2021070817465204300_ref66","doi-asserted-by":"crossref","first-page":"2796","DOI":"10.1093\/bioinformatics\/btz015","article-title":"i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2021070817465204300_ref67","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz050","article-title":"PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact","author":"Li","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021070817465204300_ref68","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.gpb.2018.08.002","article-title":"PlaD: a transcriptomics database for plant defense responses to pathogens, providing new insights into plant immune system","volume":"16","author":"Qi","year":"2018","journal-title":"Genomics Proteomics Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/2\/2126\/36669977\/bbaa049.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/2\/2126\/36669977\/bbaa049.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T01:23:03Z","timestamp":1625793783000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/22\/2\/2126\/5828119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,4]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaa049","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,3]]},"published":{"date-parts":[[2020,5,4]]}}}