Cited by 122vaccines and immunoinformatics approachesImmunotherapy and Immune ResponsesArtificial Immune Systems Applications

The DOI matches its Crossref record; 77 of the 80 references it cites were found in a registry or an index.

Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Open access

Kazi, Ada, Chuah, Candy, Majeed, Abu Bakar Abdul, Leow, Chiuan Herng, Lim, Boon Huat, Leow, Chiuan Yee·2018·Pathogens and Global Health

DOI 10.1080/20477724.2018.1446773PMC PMC6056828

Open this paper in CoChat — free account

Abstract

Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1080/20477724.2018.1446773

Cites

77 of 77 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

1 retracted

RETRACTED: Design and characterization of a chimeric multiepitope construct containing C fa B , heat‐stable toxoid, C ss A , C ss B , and heat‐labile toxin subunit B of enterotoxigenic E scherichia coli: a bioinformatic approach
Zeinalzadeh, N., Salmanian, A. H., Ahangari, G., Sadeghi, M., Amani, J., Bathaie, S. Z., & Jafari, M. (2013) · Biotechnology and Applied Biochemistry · doi:10.1002/bab.1196 · Cited by 13
Retracted
SYFPEITHI: database for MHC ligands and peptide motifs
Rammensee, H., Bachmann, J., Emmerich, N., Bachor, O. A., & Stevanović, S. (1999) · Immunogenetics · doi:10.1007/s002510050595 · Cited by 2,316
Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network
Saha, S., & Raghava, G. P. S. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21078 · Cited by 1,769
BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx346 · Cited by 1,739
ElliPro: a new structure-based tool for the prediction of antibody epitopes
Ponomarenko, J. V., Bui, H., Li, W., Fusseder, N., Bourne, P. E., Sette, A., & Peters, B. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-514 · Cited by 1,738
Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains.
Parker, K. C., Bednarek, M. A., & Coligan, J. E. (1994) · The Journal of Immunology · doi:10.4049/jimmunol.152.1.163 · Cited by 1,368
The immune epitope database (IEDB) 3.0
Vita, R., Overton, J. A., Greenbaum, J. A., Ponomarenko, J., Clark, J., Cantrell, J. R., Wheeler, D., Gabbard, J., Hix, D., Sette, A., et al. (2014) · Nucleic Acids Res. · doi:10.1093/nar/gku938 · Cited by 1,086
Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S. L., Lamberth, K., Buus, S., Brunak, S., & Lund, O. (2003) · Protein Science · doi:10.1110/ps.0239403 · Cited by 1,075
Edward Jenner and the History of Smallpox and Vaccination
Riedel, S. F. (2005) · Baylor University Medical Center Proceedings · doi:10.1080/08998280.2005.11928028 · Cited by 965
ProPred: prediction of HLA-DR binding sites
Singh, H., & Raghava, G. P. S. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.12.1236 · Cited by 844
Predicting linear B‐cell epitopes using string kernels
EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2008) · Journal of Molecular Recognition · doi:10.1002/jmr.893 · Cited by 763
AlgPred: prediction of allergenic proteins and mapping of IgE epitopes
Saha, S., & Raghava, G. P. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl343 · Cited by 745
Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices
Sturniolo, T., Bono, E. B., Ding, J., Raddrizzani, L., Tuereci, O., Şahin, U., Braxenthaler, M., Gallazzi, F., Protti, M. P., Sinigaglia, F., et al. (1999) · Nature Biotechnology · doi:10.1038/9858 · Cited by 743
Reverse vaccinology
Rappuoli, R. (2000) · Current Opinion in Microbiology · doi:10.1016/s1369-5274(00)00119-3 · Cited by 725
Reliable B cell epitope predictions: impacts of method development and improved benchmarking
Kringelum, J. V., Lundegaard, C., Lund, O., & Nielsen, M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002829 · Cited by 697
Reverse Vaccinology: Developing Vaccines in the Era of Genomics
Sette, A., & Rappuoli, R. (2010) · Immunity · doi:10.1016/j.immuni.2010.09.017 · Cited by 575
Prediction of linear B-cell epitopes using amino acid pair antigenicity scale
Chen, J., Liu, H., Yang, J., & Chou, K.-C. (2007) · Amino Acids · doi:10.1007/s00726-006-0485-9 · Cited by 552
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
Hundal, J., Carreno, B. M., Petti, A. A., Linette, G. P., Griffith, O. L., Mardis, E. R., & Griffith, M. (2016) · Genome Medicine · doi:10.1186/s13073-016-0264-5 · Cited by 454
ProPred1: prediction of promiscuous MHC Class-I binding sites
Singh, H., & Raghava, G. P. S. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg108 · Cited by 442
An overview of bioinformatics tools for epitope prediction: Implications on vaccine development
Soria‐Guerra, R. E., Nieto‐Gómez, R., Govea‐Alonso, D. O., & Rosales‐Mendoza, S. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.11.003 · Cited by 441
Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence
Singh, H., Ansari, H. R., & Raghava, G. P. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062216 · Cited by 400
SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity
Yao, B., Zhang, L., Liang, S., & Zhang, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045152 · Cited by 386
Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles
Reche, P. A., Glutting, J.-P., Zhang, H., & Reinherz, E. L. (2004) · Immunogenetics · doi:10.1007/s00251-004-0709-7 · Cited by 384
Synthetic Biology and Personalized Medicine
Jain, K. (2012) · Medical Principles and Practice · doi:10.1159/000341794 · Cited by 380
Prediction of MHC class I binding peptides using profile motifs
Reche, P. A., Glutting, J.-P., & Reinherz, E. L. (2002) · Human Immunology · doi:10.1016/s0198-8859(02)00432-9 · Cited by 376
An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction
Potocnakova, L., Bhide, M. R., & Pulzová, L. (2016) · Journal of Immunology Research · doi:10.1155/2016/6760830 · Cited by 371
Identification of conformational B-cell Epitopes in an antigen from its primary sequence
Ansari, H. R., & Raghava, G. P. S. (2010) · Immunome Research · doi:10.1186/1745-7580-6-6 · Cited by 362
Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development
He, Y. O., Xiang, Z., & Mobley, H. L. T. (2010) · BioMed Research International · doi:10.1155/2010/297505 · Cited by 356
Structural analysis of B-cell epitopes in antibody: protein complexes
Kringelum, J. V., Nielsen, M., Padkjær, S. B., & Lund, O. (2013) · Molecular Immunology · doi:10.1016/j.molimm.2012.06.001 · Cited by 305
The discovery of MHC restriction
Zinkernagel, R. M., & Doherty, P. C. (1997) · Immunology Today · doi:10.1016/s0167-5699(97)80008-4 · Cited by 289
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
Andreatta, M., Karosiene, E., Rasmussen, M., Stryhn, A., Buus, S., & Nielsen, M. (2015) · Immunogenetics · doi:10.1007/s00251-015-0873-y · Cited by 289
PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure
Sweredoski, M. J., & Baldi, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn199 · Cited by 263
MHCPred: a server for quantitative prediction of peptide-MHC binding
Guan, P. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg510 · Cited by 261
Bcipep: A database of B-cell epitopes
Saha, S., Bhasin, M., & Raghava, G. P. S. (2005) · BMC Genomics · doi:10.1186/1471-2164-6-79 · Cited by 214
PREDICTING FLEXIBLE LENGTH LINEAR B-CELL EPITOPES
EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2008) · doi:10.1142/9781848162648_0011 · Cited by 214
Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes
Paul, S., Arlehamn, C. S. L., Scriba, T. J., Dillon, M., Oseroff, C., Hinz, D., McKinney, D. M., Pro, S. C., Sidney, J., Peters, B., et al. (2015) · Journal of Immunological Methods · doi:10.1016/j.jim.2015.03.022 · Cited by 209
Immunoinformatics and epitope prediction in the age of genomic medicine
Backert, L., & Kohlbacher, O. (2015) · Genome Medicine · doi:10.1186/s13073-015-0245-0 · Cited by 205
Allermatch™, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines
Fiers, M. W. E. J., Kleter, G. A., Nijland, H., Peijnenburg, A., Nap, J. P., & van Ham, R. C. H. J. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-133 · Cited by 182
EpiJen: a server for multistep T cell epitope prediction
Doytchinova, I. A., Guan, P., & Flower, D. R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-131 · Cited by 178
Improving B-cell epitope prediction and its application to global antibody–antigen docking
Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu190 · Cited by 174
The use of genomics in microbial vaccine development
Bambini, S., & Rappuoli, R. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2008.12.007 · Cited by 165
Emerging Vaccine Informatics
He, Y. O., Rappuoli, R., Groot, A. S. D., & Chen, R. T. (2010) · BioMed Research International · doi:10.1155/2010/218590 · Cited by 154
Post‐genomic vaccine development
Serruto, D., & Rappuoli, R. (2006) · FEBS Letters · doi:10.1016/j.febslet.2006.04.084 · Cited by 145
A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes
Bhasin, M., & Raghava, G. P. S. (2007) · Journal of Biosciences · doi:10.1007/s12038-007-0004-5 · Cited by 140
Efficient peptide–MHC-I binding prediction for alleles with few known binders
Jacob, L., & Vert, J. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm611 · Cited by 137
Prediction of β‐turns in proteins from multiple alignment using neural network
Kaur, H., & Raghava, G. P. S. (2003) · Protein Science · doi:10.1110/ps.0228903 · Cited by 136
Analyses of Brucella Pathogenesis, Host Immunity, and Vaccine Targets using Systems Biology and Bioinformatics
He, Y. O. (2012) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2012.00002 · Cited by 121
SVRMHC prediction server for MHC-binding peptides
Wan, J., Liu, W., Xu, Q., Ren, Y., Flower, D. R., & Li, T. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-463 · Cited by 100
Protein Science
(2018) · Protein Science · doi:10.1110/(issn)1469-896x · Cited by 100
Immunoinformatics: Current trends and future directions
Tong, J. C., & Ren, E. C. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.04.001 · Cited by 86
In silico-based vaccine design against Ebola virus glycoprotein
Dash, R., Das, R., Junaid, M., Akash, M. F. C., Islam, A., & Hosen, S. M. Z. (2017) · Advances and Applications in Bioinformatics and Chemistry · doi:10.2147/aabc.s115859 · Cited by 85
Immunoinformatics: A Brief Review
Tomar, N., & De, R. K. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1115-8_3 · Cited by 82
Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology
Xiang, Z., & He, Y. O. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s4-s2 · Cited by 80
A novel HPV prophylactic peptide vaccine, designed by immunoinformatics and structural vaccinology approaches
Negahdaripour, M., Eslami, M., Nezafat, N., Hajighahramani, N., Ghoshoon, M. B., Shoolian, E., Dehshahri, A., Erfani, N., Morowvat, M. H., & Ghasemi, Y. (2017) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2017.08.002 · Cited by 79
Using a combined computational-experimental approach to predict antibody-specific B cell epitopes
Sela-Culang, I., Benhnia, M. R.-E.-I., Matho, M. H., Kaever, T., Maybeno, M., Schlossman, A., Nimrod, G., Li, S., Xiang, Y., Zajonc, D., et al. (2014) · Structure · doi:10.1016/j.str.2014.02.003 · Cited by 69
Immunoprotective potential of in silico predicted Acinetobacter baumannii outer membrane nuclease, NucAb
Garg, N., Singh, R., Shukla, G., Capalash, N., & Sharma, P. (2015) · International Journal of Medical Microbiology · doi:10.1016/j.ijmm.2015.10.005 · Cited by 69
An In Silico Identification of Common Putative Vaccine Candidates against Treponema pallidum: A Reverse Vaccinology and Subtractive Genomics Based Approach
Jaiswal, A. K., Tiwari, S., Jamal, S. B., Barh, D., Azevedo, V., & Soares, S. D. C. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18020402 · Cited by 60
In Silico Analysis of Epitope-Based Vaccine Candidates against Hepatitis B Virus Polymerase Protein
Zheng, J., Lin, X., Wang, X., Zheng, L., Lan, S., Jin, S., Ou, Z., & Wu, J. (2017) · Viruses · doi:10.3390/v9050112 · Cited by 56
Development of a recombinant, chimeric tetravalent dengue vaccine candidate
Osorio, J. E., Partidos, C. D., Wallace, D., & Stinchcomb, D. T. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.11.022 · Cited by 54
A simple reference state makes a significant improvement in near‐native selections from structurally refined docking decoys
Liang, S., Liu, S., Zhang, C., & Zhou, Y. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21498 · Cited by 52
Improved Proteomic Approach for the Discovery of Potential Vaccine Targets in Trypanosoma cruzi
Nakayasu, E. S., Sobreira, T. J. P., Torres, R., Ganiko, L., de Oliveira, P. S. L., Marques, A. F., & de Almeida, I. C. (2011) · Journal of Proteome Research · doi:10.1021/pr200806s · Cited by 51
Immunogenic and Invasive Properties of Brucella melitensis 16M Outer Membrane Protein Vaccine Candidates Identified via a Reverse Vaccinology Approach
Gomez, G., Pei, J., Mwangi, W., Adams, L. G., Rice‐Ficht, A. C., & Ficht, T. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059751 · Cited by 48
Prediction of Promiscuous and High-Affinity Mutated MHC Binders
Bhasin, M., & Raghava, G. P. S. (2003) · Hybridoma and Hybridomics · doi:10.1089/153685903322328956 · Cited by 43
A Population Response Analysis Approach To Assign Class II HLA-Epitope Restrictions
Paul, S., Dillon, M., Arlehamn, C. S. L., Huang, H., Davis, M. M., McKinney, D. M., Scriba, T. J., Sidney, J., Peters, B., & Sette, A. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1403074 · Cited by 41
EpiToolKit—a web-based workbench for vaccine design
Schubert, B., Brachvogel, H.-P., Jürges, C., & Kohlbacher, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv116 · Cited by 36
In silicoanalysis for identifying potential vaccine candidates againstStaphylococcus aureus
Delfani, S., Fooladi, A. A. I., Mobarez, A. M., Emaneini, M., Amani, J., & Sedighian, H. (2015) · Clinical and Experimental Vaccine Research · doi:10.7774/cevr.2015.4.1.99 · Cited by 31
New ways to identify novel bacterial antigens for vaccine development
Movahedi, A., & Hampson, D. J. (2008) · Veterinary Microbiology · doi:10.1016/j.vetmic.2008.02.011 · Cited by 27
Towardsin silicodesign of epitope-based vaccines
Toussaint, N. C., & Kohlbacher, O. (2009) · Expert Opinion on Drug Discovery · doi:10.1517/17460440903242283 · Cited by 22
Computational Identification and Characterization of a Promiscuous T-Cell Epitope on the Extracellular Protein 85B ofMycobacteriumspp. for Peptide-Based Subunit Vaccine Design
Hossain, M. S., Azad, A. K., Chowdhury, P. A., & Wakayama, M. (2017) · BioMed Research International · doi:10.1155/2017/4826030 · Cited by 17
Harnessing the power of genomics and immunoinformatics to produce improved vaccines
Moise, L., Cousens, L. P., Fueyo, J., & Groot, A. S. D. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2011.534454 · Cited by 14
A PubMed-Wide Associational Study of Infectious Diseases
Sintchenko, V., Anthony, S., Phan, X.-H., Lin, F. P. Y., & Coiera, E. W. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009535 · Cited by 13
Immunoinformatics
De, R. K., & Tomar, N. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1115-8 · Cited by 13
Predicted B-cell epitopes of HER-2 oncoprotein by a bioinformatics method: a clue for breast cancer vaccine development.
Wiwanitkit, V. (2007) · PubMed · Cited by 12
Design and in silico analysis of pentavalent chimeric antigen against three enteropathogenic bacteria: enterotoxigenic E. coli, enterohemorragic E. coli and Shigella
Hajizade, A., Firouz, Amani, J., Arpanaei, A., & Salmanian, A. H. (2016) · Bioscience Biotechnology Research Communications · doi:10.21786/bbrc/9.1/9 · Cited by 6
Computational Systems Bioinformatics
(2008) · doi:10.1142/p585 · Cited by 5
IN-SILICO APPROACH FOR PREDICTION OF VACCINE POTENTIAL ANTIGENIC PEPTIDES FROM 23-kDa TRANSMEMBRANE ANTIGEN PROTEIN OF Schistosoma haematobium
Gomase, V. S., Chitlange, N. R., Changbhale, S., Sherkhane, A. S., & K.V., K. (2012) · International Journal of Bioinformatics Research · doi:10.9735/0975-3087.4.3.276-281 · Cited by 4
Immunoinformatics and Systems Biology in Personalized Medicine
López–Campos, G., Bermejo-Martín, J. F., Almansa, R., & Martin-Sanchez, F. J. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1115-8_25 · Cited by 1

Research works printed but not linked: 5

10.1007/978-1-4939-1115-8 · DOI
10.1002/jmr.v21:4 · DOI
10.1002/bab.2014.61.issue-5 · DOI
Saha SaR GPS. editor BcePred: Prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties2004. Catania Sicily, Italy: Artificial Immune Systems: Third international conference, ICARIS; 2004 Sep 13–16.
10.1002/prot.v69:2 · DOI

Sorted by citations

Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219
“From a therapeutic perspective, knowledge of the epitope can be used for rational design in targeting an immunogenic region for vaccine development [162].”
Background
Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: an immunoinformatics approach
Ayyagari, V. S., Venkateswarulu, T. C., Peele, K. A., & Srirama, K. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1850357 · Cited by 146
“…with the conventional approaches and there are examples of the successful development of vaccine candidates by adopting in silico approaches (Ada et al., 2018; Bhatnager et al., 2020; He et al., 2010; Kim et al., 2019; Oli et al., 2020; Patronov & Doytchinova, 2013; Tordello et al., 2017…”
Background
Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach
Naz, A., Shahid, F., Butt, T. T., Awan, F. M., Ali, A., & Malik, A. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01663 · Cited by 130
Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches
Safavi, A., Kefayat, A., Mahdevar, E., Abiri, A., & Ghahremani, F. H. (2020) · Vaccine · doi:10.1016/j.vaccine.2020.10.016 · Cited by 124
“However, recent advances in the field of immunological bioinformatics have provided feasible tools which can significantly decrease the time and cost required for vaccine development [3,4].”
The advances in brucellosis vaccines
Hou, H., Liu, X., & Peng, Q. (2019) · Vaccine · doi:10.1016/j.vaccine.2019.05.084 · Cited by 117

See all 122

Authors: Kazi, A., Chuah, C., Majeed, A. B. A., Leow, C. H., Lim, B. H., & Leow, C. Y.
Affiliations: Universiti Sains Malaysia (MY)
Universiti Teknologi MARA System (MY)
,
ORCID: Kazi, Ada
Chuah, Candy
Majeed, Abu Bakar Abdul
Leow, Chiuan Herng
Lim, Boon Huat
Leow, Chiuan Yee
Venue: Pathogens and Global Health
Publisher: Informa UK Limited
Published: 2018-03-12
Volume: 112
Issue: 3
Pages: 123-131
ISSN: 2047-7724 (ISSN-L)
2047-7732
DOI: 10.1080/20477724.2018.1446773
PMC: PMC6056828
OpenAlex: W2789811841
Cited by: 122
References: 80
Citations by year: 2026: 6 · 2025: 19 · 2024: 12 · 2023: 17 · 2022: 9 · 2021: 28 · 2020: 22 · 2019: 6 · 2018: 3
FWCI: 5.25
Type: journal article
Language: en
Update notices: correction (2018-04-27)
Open access: green
www.ncbi.nlm.nih.gov
Locations: Pathogens and Global Health (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Higher Institutions Centre of Excellence Program (311/CIPPM/4401005)
Fundamental Research Grant Scheme (203/CIPPM/6171199)
The Malaysian Ministry of Higher Education of the Higher Institutions Centre of Excellence Program (311/CIPPM/4401005)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords: immunoinformatics, vaccine design, antibody design, epitope-based vaccine, immune system modeling, B-cell epitopes
MeSH terms: Animals, Epitopes, Humans, Immunity, Cellular, Vaccines, Computational Biology, Drug Discovery, Immunity, Humoral

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks