{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:56:56Z","timestamp":1787497016522,"version":"build-2736575974"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"S16","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s12859-017-1972-6","type":"journal-article","created":{"date-parts":[[2017,12,28]],"date-time":"2017-12-28T03:29:38Z","timestamp":1514431778000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["CNN-BLPred: a Convolutional neural network based predictor for \u03b2-Lactamases (BL) and their classes"],"prefix":"10.1186","volume":"18","author":[{"given":"Clarence","family":"White","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid D.","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroto","family":"Saigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dukka B.","family":"KC","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,12,28]]},"reference":[{"issue":"8","key":"1972_CR1","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/ja.2010.62","volume":"63","author":"S Donadio","year":"2010","unstructured":"Donadio S, et al. Antibiotic discovery in the twenty-first century: current trends and future perspectives. J Antibiot (Tokyo). 2010;63(8):423\u201330.","journal-title":"J Antibiot (Tokyo)"},{"issue":"2","key":"1972_CR2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1078\/1438-4221-00198","volume":"292","author":"FK Majiduddin","year":"2002","unstructured":"Majiduddin FK, Materon IC, Palzkill TG. Molecular analysis of beta-lactamase structure and function. Int J Med Microbiol. 2002;292(2):127\u201337.","journal-title":"Int J Med Microbiol"},{"issue":"3","key":"1972_CR3","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1128\/AAC.01009-09","volume":"54","author":"K Bush","year":"2010","unstructured":"Bush K, Jacoby GA. Updated functional classification of beta-lactamases. Antimicrob Agents Chemother. 2010;54(3):969\u201376.","journal-title":"Antimicrob Agents Chemother"},{"issue":"Database issue","key":"1972_CR4","doi-asserted-by":"crossref","first-page":"D443","DOI":"10.1093\/nar\/gkn656","volume":"37","author":"B Liu","year":"2009","unstructured":"Liu B, Pop M. ARDB--antibiotic resistance genes database. Nucleic Acids Res. 2009;37(Database issue):D443\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"19","key":"1972_CR5","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1093\/bioinformatics\/btt417","volume":"29","author":"M Danishuddin","year":"2013","unstructured":"Danishuddin M, et al. BLAD: a comprehensive database of widely circulated beta-lactamases. Bioinformatics. 2013;29(19):2515\u20136.","journal-title":"Bioinformatics"},{"key":"1972_CR6","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1186\/1471-2164-11-563","volume":"11","author":"QK Thai","year":"2010","unstructured":"Thai QK, Pleiss J. SHV Lactamase engineering database: a reconciliation tool for SHV beta-lactamases in public databases. BMC Genomics. 2010;11:563.","journal-title":"BMC Genomics"},{"issue":"4","key":"1972_CR7","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1128\/AAC.50.4.1123-1129.2006","volume":"50","author":"GA Jacoby","year":"2006","unstructured":"Jacoby GA. Beta-lactamase nomenclature. Antimicrob Agents Chemother. 2006;50(4):1123\u20139.","journal-title":"Antimicrob Agents Chemother"},{"issue":"2","key":"1972_CR8","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1021\/cr030102i","volume":"105","author":"JF Fisher","year":"2005","unstructured":"Fisher JF, Meroueh SO, Mobashery S. Bacterial resistance to beta-lactam antibiotics: compelling opportunism, compelling opportunity. Chem Rev. 2005;105(2):395\u2013424.","journal-title":"Chem Rev"},{"issue":"6","key":"1972_CR9","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1128\/AAC.39.6.1211","volume":"39","author":"K Bush","year":"1995","unstructured":"Bush K, Jacoby GA, Medeiros AA. A functional classification scheme for beta-lactamases and its correlation with molecular structure. Antimicrob Agents Chemother. 1995;39(6):1211\u201333.","journal-title":"Antimicrob Agents Chemother"},{"key":"1972_CR10","first-page":"980572","volume":"2014","author":"A Srivastava","year":"2014","unstructured":"Srivastava A, et al. Identification of family specific fingerprints in beta-lactamase families. ScientificWorldJournal. 2014;2014:980572.","journal-title":"ScientificWorldJournal"},{"key":"1972_CR11","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jtbi.2014.10.008","volume":"365","author":"R Kumar","year":"2015","unstructured":"Kumar R, et al. Prediction of beta-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine. J Theor Biol. 2015;365:96\u2013103.","journal-title":"J Theor Biol"},{"issue":"2","key":"1972_CR12","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1006\/bbrc.2000.3815","volume":"278","author":"KC Chou","year":"2000","unstructured":"Chou KC. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem Biophys Res Commun. 2000;278(2):477\u201383.","journal-title":"Biochem Biophys Res Commun"},{"key":"1972_CR13","unstructured":"Ismail H, Saigo H, KC DB. RF-NR: Random forest based approach for improved classification of Nuclear Receptors. IEEE\/ACM Trans Comput BIol Bioinfom, pp. 1\u20131, 2017. (Also appeared in GIW\/INCoB 2015, Tokyo, Japan)."},{"key":"1972_CR14","doi-asserted-by":"crossref","first-page":"3281590","DOI":"10.1155\/2016\/3281590","volume":"2016","author":"HD Ismail","year":"2016","unstructured":"Ismail HD, et al. RF-Phos: a novel general Phosphorylation site prediction tool based on random Forest. Biomed Res Int. 2016;2016:3281590.","journal-title":"Biomed Res Int"},{"key":"1972_CR15","doi-asserted-by":"crossref","unstructured":"Ismail HD, Newman RH, Kc DB. RF-Hydroxysite: a random forest based predictor for hydroxylation sites. Mol BioSyst. 2016;12:\u20132427.","DOI":"10.1039\/C6MB00179C"},{"issue":"11","key":"1972_CR16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"issue":"7553","key":"1972_CR17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"key":"1972_CR18","doi-asserted-by":"publisher","unstructured":"Min S, Lee B, Yoon S.Deep learning in bioinformatics.Brief Bioinform. 2017;18(5):851\u201369. doi: 10.1093\/bib\/bbw068 .","DOI":"10.1093\/bib\/bbw068"},{"issue":"7","key":"1972_CR19","doi-asserted-by":"crossref","first-page":"e67863","DOI":"10.1371\/journal.pone.0067863","volume":"8","author":"Q Wei","year":"2013","unstructured":"Wei Q, Dunbrack RL Jr. The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One. 2013;8(7):e67863.","journal-title":"PLoS One"},{"issue":"13","key":"1972_CR20","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","volume":"22","author":"W Li","year":"2006","unstructured":"Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658\u20139.","journal-title":"Bioinformatics"},{"key":"1972_CR21","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, et al. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"issue":"11","key":"1972_CR22","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1073\/pnas.0607879104","volume":"104","author":"J Shen","year":"2007","unstructured":"Shen J, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A. 2007;104(11):4337\u201341.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"1972_CR23","doi-asserted-by":"crossref","unstructured":"Yin Z, T.J. New encoding schemes for prediction of protein phosphorylation sites. in 2012 IEEE 6th International Conference on Systems Biology (ISB). Xi'an: IEEE; 2012.","DOI":"10.1109\/ISB.2012.6314113"},{"key":"1972_CR24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/1472-6807-7-25","volume":"7","author":"K Chen","year":"2007","unstructured":"Chen K, Kurgan LA, Ruan J. Prediction of flexible\/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol. 2007;7:25.","journal-title":"BMC Struct Biol"},{"key":"1972_CR25","volume-title":"XGBoost: A Scalable Tree Boosting System. in KDD\u201916","author":"T Chen","year":"2016","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. in KDD\u201916. San Francisco: ACM; 2016."},{"key":"1972_CR26","volume-title":"Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems","author":"T Mikolov","year":"2013","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 2013."},{"issue":"9","key":"1972_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3022670.2976746","volume":"51","author":"M Abadi","year":"2016","unstructured":"Abadi M. TensorFlow: learning functions at scale. ACM SIGPLAN Not. 2016;51(9):1\u20131.","journal-title":"ACM SIGPLAN Not"},{"key":"1972_CR28","unstructured":"Tang Y. TF.Learn: TensorFlow's high-level module for distributed machine learning. CoRR, 2016;vol. abs\/1612.04251."},{"issue":"5","key":"1972_CR29","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.ijantimicag.2016.08.013","volume":"48","author":"PL Lu","year":"2016","unstructured":"Lu PL, et al. Characterisation of fosfomycin resistance mechanisms and molecular epidemiology in extended-spectrum beta-lactamase-producing Klebsiella Pneumoniae isolates. Int J Antimicrob Agents. 2016;48(5):564\u20138.","journal-title":"Int J Antimicrob Agents"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-017-1972-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,8]],"date-time":"2019-10-08T15:58:46Z","timestamp":1570550326000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-017-1972-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":29,"journal-issue":{"issue":"S16","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["1972"],"URL":"https:\/\/doi.org\/10.1186\/s12859-017-1972-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12]]},"article-number":"577"}}