Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates

[1]  P. Hotez,et al.  Identification of vaccine targets in pathogens and design of a vaccine using computational approaches , 2021, Scientific Reports.

[2]  J. Z. Kolter,et al.  Overfitting in adversarially robust deep learning , 2020, ICML.

[3]  Yongqun He,et al.  Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens , 2020, Bioinform..

[4]  V. Azevedo,et al.  Prediction of new vaccine targets in the core genome of Corynebacterium pseudotuberculosis through omics approaches and reverse vaccinology. , 2019, Gene.

[5]  Jamil Ahmad,et al.  PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome , 2019, BMC Bioinformatics.

[6]  Rino Rappuoli,et al.  Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery , 2019, Front. Immunol..

[7]  Ming Zhang,et al.  Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs , 2019, Neurocomputing.

[8]  Vandana Solanki,et al.  Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii , 2018, Scientific Reports.

[9]  Jan N. van Rijn,et al.  Hyperparameter Importance Across Datasets , 2017, KDD.

[10]  Laura A. Muruato,et al.  Use of Reverse Vaccinology in the Design and Construction of Nanoglycoconjugate Vaccines against Burkholderia pseudomallei , 2017, Clinical and Vaccine Immunology.

[11]  Jamil Ahmad,et al.  VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology , 2017, BMC Bioinformatics.

[12]  Mahesan Niranjan,et al.  Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology , 2017, International journal of molecular sciences.

[13]  Karin M. Verspoor,et al.  Supervised Learning for Detection of Duplicates in Genomic Sequence Databases , 2016, PloS one.

[14]  Gajendra P. S. Raghava,et al.  Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics , 2016, Briefings Bioinform..

[15]  Dong-Sheng Cao,et al.  protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences , 2015, Bioinform..

[16]  Rosa María Ribas-Aparicio,et al.  Identification of Novel Potential Vaccine Candidates against Tuberculosis Based on Reverse Vaccinology , 2015, BioMed research international.

[17]  Christian Szegedy,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[18]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[19]  Paul J. Kennedy,et al.  Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology , 2014, Bioinform..

[20]  Rebecca Racz,et al.  Updates on the web-based VIOLIN vaccine database and analysis system , 2013, Nucleic Acids Res..

[21]  Paul J. Kennedy,et al.  A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms , 2013, BMC Bioinformatics.

[22]  Ankit Gupta,et al.  Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions , 2013, BMC Bioinformatics.

[23]  J. Liebenberg,et al.  Identification of Ehrlichia ruminantium proteins that activate cellular immune responses using a reverse vaccinology strategy. , 2012, Veterinary immunology and immunopathology.

[24]  Faramarz Valafar,et al.  Improving reverse vaccinology with a machine learning approach. , 2011, Vaccine.

[25]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[26]  T. Aebischer,et al.  Vaccines for Leishmaniasis: From proteome to vaccine candidates , 2011, Human vaccines.

[27]  Pierre Baldi,et al.  High-throughput prediction of protein antigenicity using protein microarray data , 2010, Bioinform..

[28]  Yongqun He,et al.  Protegen: a web-based protective antigen database and analysis system , 2010, Nucleic Acids Res..

[29]  Yongqun He,et al.  Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development , 2010, Journal of biomedicine & biotechnology.

[30]  Gary D Stormo,et al.  An Introduction to Sequence Similarity (“Homology”) Searching , 2009, Current protocols in bioinformatics.

[31]  Fang Chen,et al.  VIOLIN: vaccine investigation and online information network , 2007, Nucleic Acids Res..

[32]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[33]  Zhi-Wei Cao,et al.  Efficacy of different protein descriptors in predicting protein functional families , 2007, BMC Bioinformatics.

[34]  Francesco Filippini,et al.  NERVE: New Enhanced Reverse Vaccinology Environment , 2006, BMC biotechnology.

[35]  Bryan Lingard,et al.  Analysis of Known Bacterial Protein Vaccine Antigens Reveals Biased Physical Properties and Amino Acid Composition , 2003, Comparative and functional genomics.

[36]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Scott Fields.  New Cell Growth , 2002, Environmental health perspectives.

[38]  K. Chou.  Prediction of protein cellular attributes using pseudo‐amino acid composition , 2001, Proteins.

[39]  K. Chou,et al.  Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. , 2000, Biochemical and biophysical research communications.

[40]  J. Venter,et al.  Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. , 2000, Science.

[41]  Minoru Kanehisa,et al.  AAindex: Amino Acid index database , 2000, Nucleic Acids Res..

[42]  Hiroyuki Ogata,et al.  AAindex: Amino Acid Index Database , 1999, Nucleic Acids Res..