{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T23:17:15Z","timestamp":1778887035933,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","funder":[{"name":"National Research Foundation (NRF) South Africa","award":["93690"],"award-info":[{"award-number":["93690"]}]},{"name":"National Research Foundation (NRF) South Africa","award":["80983"],"award-info":[{"award-number":["80983"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s12859-017-1782-x","type":"journal-article","created":{"date-parts":[[2017,8,15]],"date-time":"2017-08-15T12:02:58Z","timestamp":1502798578000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks"],"prefix":"10.1186","volume":"18","author":[{"given":"Olivier","family":"Sheik Amamuddy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nigel T.","family":"Bishop","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-7849","authenticated-orcid":false,"given":"\u00d6zlem","family":"Tastan Bishop","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,8,15]]},"reference":[{"key":"1782_CR1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/1744-8603-7-35","volume":"7","author":"L Reynolds","year":"2011","unstructured":"Reynolds L. HIV as a chronic disease considerations for service planning in resource-poor settings. Glob Health. 2011;7:35.","journal-title":"Glob Health"},{"key":"1782_CR2","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/S1473-3099(11)70097-4","volume":"11","author":"F Zhang","year":"2011","unstructured":"Zhang F, Dou Z, Ma Y, Zhang Y, Zhao Y, Zhao D, et al. Effect of earlier initiation of antiretroviral treatment and increased treatment coverage on HIV-related mortality in China: a national observational cohort study. Lancet Infect Dis. 2011;11:516\u201324.","journal-title":"Lancet Infect Dis"},{"key":"1782_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/annotation\/c4d2aff9-0c5c-4ebb-b1ed-efa69fc84d78","volume":"8","author":"H Xing","year":"2013","unstructured":"Xing H, Ruan Y, Li J, Shang H, Zhong P, Wang X, et al. HIV drug resistance and its impact on antiretroviral therapy in Chinese HIV-infected patients. PLoS One. 2013;8:1\u20137.","journal-title":"PLoS One"},{"key":"1782_CR4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/00359190909519238","volume":"64","author":"ST Araya","year":"2009","unstructured":"Araya ST, Hazelhurst S. Support vector machine prediction of HIV-1 drug resistance using the viral nucleotide patterns. Trans R Soc South Africa. 2009;64:62\u201372.","journal-title":"Trans R Soc South Africa"},{"key":"1782_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2165\/11633630-000000000-00000","volume":"72","author":"MW Tang","year":"2012","unstructured":"Tang MW, Shafer RW. HIV-1 antiretroviral resistance: Scientific principles and clinical applications. Drugs. 2012;72:1\u201325.","journal-title":"Drugs"},{"key":"1782_CR6","first-page":"145","volume":"14","author":"MCF Prosperi","year":"2012","unstructured":"Prosperi MCF, De Luca A. Computational models for prediction of response to antiretroviral therapies. AIDS Rev. 2012;14:145\u201353.","journal-title":"AIDS Rev"},{"key":"1782_CR7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1093\/bioinformatics\/19.1.98","volume":"19","author":"S Dr\u0103ghici","year":"2003","unstructured":"Dr\u0103ghici S, Potter RB. Predicting HIV drug resistance with neural networks. Bioinformatics. 2003;19:98\u2013107.","journal-title":"Bioinformatics"},{"key":"1782_CR8","doi-asserted-by":"crossref","unstructured":"Riemenschneider M, Heider D. Current Approaches in Computational Drug Resistance Prediction in HIV. Curr HIV Res 2016;1\u20139.","DOI":"10.2174\/1570162X14666160321120232"},{"key":"1782_CR9","first-page":"642","volume":"22","author":"AM Wensing","year":"2014","unstructured":"Wensing AM, Calvez V, G\u00fcnthard HF, Johnson VA, Paredes R, Pillay D, et al. 2014 update of the drug resistance mutations in HIV-1. Top. Antivir. Med. 2014;22:642\u201350.","journal-title":"Top. Antivir. Med."},{"key":"1782_CR10","first-page":"132","volume":"23","author":"AM Wensing","year":"2015","unstructured":"Wensing AM, Calvez V, G\u00fcnthard HF, Johnson VA, Paredes R, Pillay D, et al. 2015 update of the drug resistance mutations in HIV-1. Top Antivir Med. 2015;23:132\u201341.","journal-title":"Top Antivir Med"},{"key":"1782_CR11","doi-asserted-by":"crossref","first-page":"661","DOI":"10.3851\/IMP2947","volume":"20","author":"S Wagner","year":"2015","unstructured":"Wagner S, Kurz M, Klimkait T. Algorithm evolution for drug resistance prediction: comparison of systems for HIV-1 genotyping. Antivir Ther. 2015;20:661\u20135.","journal-title":"Antivir Ther"},{"key":"1782_CR12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1177\/135965350200700206","volume":"7","author":"K Laethem Van","year":"2002","unstructured":"Van Laethem K, De Luca A, Antinori A, Cingolani A, Perno CF, Vandamme AM. A genotypic drug resistance interpretation algorithm that significantly predicts therapy response in HIV-1-infected patients. Antivir Ther. 2002;7:123\u20139.","journal-title":"Antivir Ther"},{"key":"1782_CR13","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1097\/00002030-200203290-00008","volume":"16","author":"J-L Meynard","year":"2002","unstructured":"Meynard J-L, Vray M, Morand-Joubert L, Race E, Descamps D, Peytavin G, et al. Phenotypic or genotypic resistance testing for choosing antiretroviral therapy after treatment failure: a randomized trial. AIDS. 2002;16:727\u201336.","journal-title":"AIDS"},{"key":"1782_CR14","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1093\/nar\/gkg100","volume":"31","author":"S-Y Rhee","year":"2003","unstructured":"Rhee S-Y, Gonzales MJ, Kantor R, Betts BJ, Ravela J, Shafer RW. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res. 2003;31:298\u2013303.","journal-title":"Nucleic Acids Res"},{"key":"1782_CR15","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1038\/nrmicro1477","volume":"4","author":"T Lengauer","year":"2006","unstructured":"Lengauer T, Sing T. Bioinformatics-assisted anti-HIV therapy. Nat Rev Microbiol. 2006;4:790\u20137.","journal-title":"Nat Rev Microbiol"},{"key":"1782_CR16","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1086\/503914","volume":"42","author":"TF Liu","year":"2006","unstructured":"Liu TF, Shafer RW. Web resources for HIV type 1 genotypic-resistance test interpretation. Clin Infect Dis. 2006;42:1608\u201318.","journal-title":"Clin Infect Dis"},{"key":"1782_CR17","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1186\/s12859-016-1179-2","volume":"17","author":"M Riemenschneider","year":"2016","unstructured":"Riemenschneider M, Hummel T, Heider D. SHIVA - a web application for drug resistance and tropism testing in HIV. BMC Bioinf. 2016;17:314.","journal-title":"BMC Bioinf."},{"key":"1782_CR18","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/5254.972080","volume":"16","author":"N Beerenwinkel","year":"2001","unstructured":"Beerenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D, et al. Geno2pheno: interpreting genotypic HIV drug resistance tests. IEEE Intell Syst Their Appl. 2001;16:35\u201341.","journal-title":"IEEE Intell Syst Their Appl"},{"key":"1782_CR19","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1186\/s12859-016-1114-6","volume":"17","author":"C Shen","year":"2016","unstructured":"Shen C, Yu X, Harrison RW, Weber IT. Automated prediction of HIV drug resistance from genotype data. BMC Bioinf. 2016;17:278.","journal-title":"BMC Bioinf"},{"key":"1782_CR20","first-page":"342","volume":"2013","author":"X Yu","year":"2013","unstructured":"Yu X, Weber IT, Harrison RW. Sparse representation for prediction of HIV-1 protease drug resistance. Proc. 2013 SIAM Int. conf. Data mining. SIAM Int. conf. Data Min. 2013;2013:342\u20139.","journal-title":"Data Min"},{"key":"1782_CR21","first-page":"S1","volume":"15","author":"X Yu","year":"2014","unstructured":"Yu X, Weber IT, Harrison RW. Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure. BMC Genomics. 2014;15:S1.","journal-title":"BMC Genomics"},{"issue":"Suppl 4","key":"1782_CR22","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1186\/1471-2164-14-S4-S3","volume":"14","author":"M Masso","year":"2013","unstructured":"Masso M, Vaisman II. Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance. BMC Genomics. 2013;14(Suppl 4):S3.","journal-title":"BMC Genomics"},{"key":"1782_CR23","unstructured":"Stanford HIVdb. Genotype-Phenotype Datasets. 2014 [cited 2016 Dec 13]. Available from: https:\/\/hivdb.stanford.edu\/pages\/genopheno.dataset.html ."},{"key":"1782_CR24","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/00126334-200305010-00002","volume":"33","author":"J Ravela","year":"2003","unstructured":"Ravela J, Betts BJ, Brun-V\u00e9zinet F, Vandamme A-M, Descamps D, van Laethem K, et al. HIV-1 protease and reverse transcriptase mutation patterns responsible for discordances between genotypic drug resistance interpretation algorithms. J Acquir Immune Defic Syndr. 2003;33:8\u201314.","journal-title":"J Acquir Immune Defic Syndr"},{"key":"1782_CR25","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/S0168-9525(00)02024-2","volume":"16","author":"P Rice","year":"2000","unstructured":"Rice P, Longden I, Bleasby A. EMBOSS: the European molecular biology open software suite. Trends Genet. 2000;16:276\u20137.","journal-title":"Trends Genet"},{"key":"1782_CR26","unstructured":"Hedlin H. Genotype-Phenotype Datasets: DRMcv. 2014 [cited 2017 May 22]. Available from: https:\/\/hivdb.stanford.edu\/download\/GenoPhenoDatasets\/DRMcv.R ."},{"key":"1782_CR27","unstructured":"Monogram Biosciences. Phenosense HIV Drug Resistance Assay. 2014 [cited 2017 Jul 18]. p. 1\u20132. Available from: https:\/\/www.monogrambio.com\/sites\/monogrambio\/files\/imce\/uploads\/PS_report_new_Watermark.pdf ."},{"key":"1782_CR28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.4172\/jaa.1000148","volume":"8","author":"R Dahake","year":"2016","unstructured":"Dahake R, Mehta S, Yadav S. Polymorphisms in HIV-1 subtype C reverse transcriptase and protease genes in a patient cohort from Mumbai. J Antivir Antiretrovir. 2016;8:5\u20137.","journal-title":"J Antivir Antiretrovir"},{"key":"1782_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000369017","volume":"58","author":"S Gupta","year":"2015","unstructured":"Gupta S, Neogi U, Srinivasa H, Shet A. Performance of genotypic tools for prediction of tropism in HIV-1 subtype C V3 loop sequences. Intervirology. 2015;58:1\u20135.","journal-title":"Intervirology"},{"key":"1782_CR30","doi-asserted-by":"crossref","first-page":"24883","DOI":"10.1038\/srep24883","volume":"6","author":"M Riemenschneider","year":"2016","unstructured":"Riemenschneider M, Cashin KY, Budeus B, Sierra S, Shirvani-Dastgerdi E, Bayanolhagh S, et al. Genotypic prediction of co-receptor tropism of HIV-1 subtypes a and C. Sci Rep. 2016;6:24883.","journal-title":"Sci Rep"},{"key":"1782_CR31","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1097\/QAI.0b013e3181c8413b","volume":"53","author":"S Raymond","year":"2010","unstructured":"Raymond S, Delobel P, Mavigner M, Ferradini L, Cazabat M, Souyris C, et al. Prediction of HIV type 1 subtype C tropism by genotypic algorithms built from subtype B viruses. J Acquir Immune Defic Syndr. 2010;53:167\u201375.","journal-title":"J Acquir Immune Defic Syndr"},{"key":"1782_CR32","doi-asserted-by":"crossref","unstructured":"Awoke T, Worku A, Kebede Y, Kasim A, Birlie B, Braekers R, et al. Modeling Outcomes of First-Line Antiretroviral Therapy and Rate of CD4 Counts Change among a Cohort of HIV \/ AIDS Patients in Ethiopia: A Retrospective Cohort Study. PLoS ONE. 2016;11:1\u201318.","DOI":"10.1371\/journal.pone.0168323"},{"key":"1782_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0120350","volume":"10","author":"HC Duber","year":"2015","unstructured":"Duber HC, Dansereau E, Masters SH, Achan J, Burstein R, DeCenso B, et al. Uptake of WHO recommendations for first-line antiretroviral therapy in Kenya, Uganda, and Zambia. PLoS One. 2015;10:1\u201312.","journal-title":"PLoS One"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-017-1782-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T05:52:57Z","timestamp":1659333177000},"score":1,"resource":{"primary":{"URL":"http:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-017-1782-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,15]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["1782"],"URL":"https:\/\/doi.org\/10.1186\/s12859-017-1782-x","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,15]]},"article-number":"369"}}