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For: Jaiswal V, Chanumolu SK, Gupta A, Chauhan RS, Rout C. Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions. BMC Bioinformatics 2013;14:211. [PMID: 23815072 DOI: 10.1186/1471-2105-14-211] [Cited by in Crossref: 36] [Cited by in F6Publishing: 34] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
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5 Yero D, Conchillo-Solé O, Daura X. Antigen Discovery in Bacterial Panproteomes. Methods Mol Biol 2021;2183:43-62. [PMID: 32959240 DOI: 10.1007/978-1-0716-0795-4_5] [Reference Citation Analysis]
6 Vishambra D, Srivastava M, Dev K, Jaiswal V. Subcellular localization based comparative study on radioresistant bacteria: A novel approach to mine proteins involve in radioresistance. Comput Biol Chem 2017;69:1-9. [PMID: 28527408 DOI: 10.1016/j.compbiolchem.2017.05.002] [Cited by in Crossref: 1] [Article Influence: 0.2] [Reference Citation Analysis]
7 Pal T, Malhotra N, Chanumolu SK, Chauhan RS. Next-generation sequencing (NGS) transcriptomes reveal association of multiple genes and pathways contributing to secondary metabolites accumulation in tuberous roots of Aconitum heterophyllum Wall. Planta 2015;242:239-58. [PMID: 25904478 DOI: 10.1007/s00425-015-2304-6] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 3.6] [Reference Citation Analysis]
8 de Alvarenga Mudadu M, Carvalho V, Leclercq SY. Nonclassically secreted proteins as possible antigens for vaccine development: a reverse vaccinology approach. Appl Biochem Biotechnol 2015;175:3360-70. [PMID: 25672322 DOI: 10.1007/s12010-015-1507-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
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10 He Y, Racz R, Sayers S, Lin Y, Todd T, Hur J, Li X, Patel M, Zhao B, Chung M, Ostrow J, Sylora A, Dungarani P, Ulysse G, Kochhar K, Vidri B, Strait K, Jourdian GW, Xiang Z. Updates on the web-based VIOLIN vaccine database and analysis system. Nucleic Acids Res 2014;42:D1124-32. [PMID: 24259431 DOI: 10.1093/nar/gkt1133] [Cited by in Crossref: 40] [Cited by in F6Publishing: 39] [Article Influence: 4.4] [Reference Citation Analysis]
11 Madampage CA, Wilson D, Townsend H, Crockford G, Rawlyk N, Dent D, Evans B, Van Donkersgoed J, Dorin C, Potter A. Cattle Immunized with a Recombinant Subunit Vaccine Formulation Exhibits a Trend towards Protection against Histophilus somni Bacterial Challenge. PLoS One 2016;11:e0159070. [PMID: 27501390 DOI: 10.1371/journal.pone.0159070] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
12 Rawal K, Sinha R, Nath SK, Preeti P, Kumari P, Gupta S, Sharma T, Strych U, Hotez P, Bottazzi ME. Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates. Comput Biol Med 2022;145:105401. [PMID: 35381451 DOI: 10.1016/j.compbiomed.2022.105401] [Reference Citation Analysis]
13 Jaiswal V, Chauhan RS, Rout C. Common antigens prediction in bacterial bioweapons: a perspective for vaccine design. Infect Genet Evol 2014;21:315-9. [PMID: 24300889 DOI: 10.1016/j.meegid.2013.11.011] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
14 Guleria V, Jaiswal V. Comparative transcriptome analysis of different stages of Plasmodium falciparum to explore vaccine and drug candidates. Genomics 2020;112:796-804. [PMID: 31128264 DOI: 10.1016/j.ygeno.2019.05.018] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
15 Ong E, Wang H, Wong MU, Seetharaman M, Valdez N, He Y. Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics 2020;36:3185-91. [PMID: 32096826 DOI: 10.1093/bioinformatics/btaa119] [Cited by in Crossref: 19] [Cited by in F6Publishing: 13] [Article Influence: 9.5] [Reference Citation Analysis]
16 Heinson AI, Gunawardana Y, Moesker B, Hume CC, Vataga E, Hall Y, Stylianou E, McShane H, Williams A, Niranjan M, Woelk CH. Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology. Int J Mol Sci 2017;18:E312. [PMID: 28157153 DOI: 10.3390/ijms18020312] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 3.2] [Reference Citation Analysis]
17 Lew-Tabor AE, Rodriguez Valle M. A review of reverse vaccinology approaches for the development of vaccines against ticks and tick borne diseases. Ticks Tick Borne Dis 2016;7:573-85. [PMID: 26723274 DOI: 10.1016/j.ttbdis.2015.12.012] [Cited by in Crossref: 57] [Cited by in F6Publishing: 51] [Article Influence: 8.1] [Reference Citation Analysis]
18 Oli AN, Obialor WO, Ifeanyichukwu MO, Odimegwu DC, Okoyeh JN, Emechebe GO, Adejumo SA, Ibeanu GC. Immunoinformatics and Vaccine Development: An Overview. Immunotargets Ther 2020;9:13-30. [PMID: 32161726 DOI: 10.2147/ITT.S241064] [Cited by in Crossref: 26] [Cited by in F6Publishing: 14] [Article Influence: 13.0] [Reference Citation Analysis]
19 D'Mello A, Ahearn CP, Murphy TF, Tettelin H. ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates. BMC Genomics 2019;20:981. [PMID: 31842745 DOI: 10.1186/s12864-019-6195-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
20 Singh R, Capalash N, Sharma P. Vaccine development to control the rising scourge of antibiotic-resistant Acinetobacter baumannii: a systematic review. 3 Biotech 2022;12. [DOI: 10.1007/s13205-022-03148-9] [Reference Citation Analysis]
21 Ong E, Cooke MF, Huffman A, Xiang Z, Wong MU, Wang H, Seetharaman M, Valdez N, He Y. Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Res 2021;49:W671-8. [PMID: 34009334 DOI: 10.1093/nar/gkab279] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Rahman MS, Rahman MK, Saha S, Kaykobad M, Rahman MS. Antigenic: An improved prediction model of protective antigens. Artificial Intelligence in Medicine 2019;94:28-41. [DOI: 10.1016/j.artmed.2018.12.010] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 5.3] [Reference Citation Analysis]
23 Ong E, Wong MU, He Y. Identification of New Features from Known Bacterial Protective Vaccine Antigens Enhances Rational Vaccine Design. Front Immunol 2017;8:1382. [PMID: 29123525 DOI: 10.3389/fimmu.2017.01382] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 2.4] [Reference Citation Analysis]
24 Wang W, Liu J, Guo S, Liu L, Yuan Q, Guo L, Pan S. Identification of Vibrio parahaemolyticus and Vibrio spp. Specific Outer Membrane Proteins by Reverse Vaccinology and Surface Proteome. Front Microbiol 2020;11:625315. [PMID: 33633699 DOI: 10.3389/fmicb.2020.625315] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Mohammadzadeh R, Soleimanpour S, Pishdadian A, Farsiani H. Designing and development of epitope-based vaccines against Helicobacter pylori. Crit Rev Microbiol 2021;:1-24. [PMID: 34559599 DOI: 10.1080/1040841X.2021.1979934] [Reference Citation Analysis]
26 Inácio MM, Cruz-leite VRM, Moreira ALE, Mattos K, Paccez JD, Ruiz OH, Venturini J, de Souza Carvalho Melhem M, Paniago AMM, de Almeida Soares CM, Weber SS, Borges CL. Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets. Pathogens 2022;11:569. [DOI: 10.3390/pathogens11050569] [Reference Citation Analysis]
27 Heinson AI, Woelk CH, Newell ML. The promise of reverse vaccinology. Int Health 2015;7:85-9. [PMID: 25733557 DOI: 10.1093/inthealth/ihv002] [Cited by in Crossref: 23] [Cited by in F6Publishing: 22] [Article Influence: 3.3] [Reference Citation Analysis]
28 Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discovery Today 2020;25:1034-42. [DOI: 10.1016/j.drudis.2020.03.006] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 12.5] [Reference Citation Analysis]
29 Dalsass M, Brozzi A, Medini D, Rappuoli R. Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery. Front Immunol 2019;10:113. [PMID: 30837982 DOI: 10.3389/fimmu.2019.00113] [Cited by in Crossref: 30] [Cited by in F6Publishing: 26] [Article Influence: 10.0] [Reference Citation Analysis]
30 He Y. Ontology-supported research on vaccine efficacy, safety and integrative biological networks. Expert Rev Vaccines 2014;13:825-41. [PMID: 24909153 DOI: 10.1586/14760584.2014.923762] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 0.9] [Reference Citation Analysis]
31 Madampage CA, Rawlyk N, Crockford G, Wang Y, White AP, Brownlie R, Van Donkersgoed J, Dorin C, Potter A. Reverse vaccinology as an approach for developing Histophilus somni vaccine candidates. Biologicals 2015;43:444-51. [PMID: 26460173 DOI: 10.1016/j.biologicals.2015.09.001] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
32 Pal T, Padhan JK, Kumar P, Sood H, Chauhan RS. Comparative transcriptomics uncovers differences in photoautotrophic versus photoheterotrophic modes of nutrition in relation to secondary metabolites biosynthesis in Swertia chirayita. Mol Biol Rep 2018;45:77-98. [PMID: 29349608 DOI: 10.1007/s11033-017-4135-y] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
33 Dimitrov I, Zaharieva N, Doytchinova I. Bacterial Immunogenicity Prediction by Machine Learning Methods. Vaccines (Basel) 2020;8:E709. [PMID: 33265930 DOI: 10.3390/vaccines8040709] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
34 Liljeroos L, Malito E, Ferlenghi I, Bottomley MJ. Structural and Computational Biology in the Design of Immunogenic Vaccine Antigens. J Immunol Res 2015;2015:156241. [PMID: 26526043 DOI: 10.1155/2015/156241] [Cited by in Crossref: 47] [Cited by in F6Publishing: 40] [Article Influence: 6.7] [Reference Citation Analysis]
35 Baliga P, Shekar M, Venugopal MN. Potential Outer Membrane Protein Candidates for Vaccine Development Against the Pathogen Vibrio anguillarum: A Reverse Vaccinology Based Identification. Curr Microbiol 2018;75:368-77. [PMID: 29119233 DOI: 10.1007/s00284-017-1390-z] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 2.2] [Reference Citation Analysis]
36 Pasala C, Chilamakuri CSR, Katari SK, Nalamolu RM, Bitla AR, Amineni U. Epitope-driven common subunit vaccine design against H. pylori strains. Journal of Biomolecular Structure and Dynamics 2019;37:3740-50. [DOI: 10.1080/07391102.2018.1526714] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 2.3] [Reference Citation Analysis]
37 Moise L, Gutierrez A, Kibria F, Martin R, Tassone R, Liu R, Terry F, Martin B, De Groot AS. iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines. Hum Vaccin Immunother 2015;11:2312-21. [PMID: 26155959 DOI: 10.1080/21645515.2015.1061159] [Cited by in Crossref: 52] [Cited by in F6Publishing: 48] [Article Influence: 8.7] [Reference Citation Analysis]
38 Bragazzi NL, Gianfredi V, Villarini M, Rosselli R, Nasr A, Hussein A, Martini M, Behzadifar M. Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview. Front Public Health 2018;6:62. [PMID: 29556492 DOI: 10.3389/fpubh.2018.00062] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 7.0] [Reference Citation Analysis]