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Items: 1 to 20 of 83

1.

Improving reverse vaccinology with a machine learning approach.

Bowman BN, McAdam PR, Vivona S, Zhang JX, Luong T, Belew RK, Sahota H, Guiney D, Valafar F, Fierer J, Woelk CH.

Vaccine. 2011 Oct 19;29(45):8156-64. doi: 10.1016/j.vaccine.2011.07.142.

PMID:
21864619
2.

NERVE: new enhanced reverse vaccinology environment.

Vivona S, Bernante F, Filippini F.

BMC Biotechnol. 2006 Jul 18;6:35.

3.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions.

Jaiswal V, Chanumolu SK, Gupta A, Chauhan RS, Rout C.

BMC Bioinformatics. 2013 Jul 1;14:211. doi: 10.1186/1471-2105-14-211.

4.
5.

Large-scale learning of structure-activity relationships using a linear support vector machine and problem-specific metrics.

Hinselmann G, Rosenbaum L, Jahn A, Fechner N, Ostermann C, Zell A.

J Chem Inf Model. 2011 Feb 28;51(2):203-13. doi: 10.1021/ci100073w.

PMID:
21207929
6.

The promise of reverse vaccinology.

Heinson AI, Woelk CH, Newell ML.

Int Health. 2015 Mar;7(2):85-9. doi: 10.1093/inthealth/ihv002. Review.

PMID:
25733557
7.

Developing vaccines in the era of genomics: a decade of reverse vaccinology.

Seib KL, Zhao X, Rappuoli R.

Clin Microbiol Infect. 2012 Oct;18 Suppl 5:109-16. doi: 10.1111/j.1469-0691.2012.03939.x. Review.

8.

Reverse vaccinology as an approach for developing Histophilus somni vaccine candidates.

Madampage CA, Rawlyk N, Crockford G, Wang Y, White AP, Brownlie R, Van Donkersgoed J, Dorin C, Potter A.

Biologicals. 2015 Nov;43(6):444-51. doi: 10.1016/j.biologicals.2015.09.001.

PMID:
26460173
9.

Machine learning approaches for prediction of linear B-cell epitopes on proteins.

Söllner J, Mayer B.

J Mol Recognit. 2006 May-Jun;19(3):200-8.

PMID:
16598694
10.

Antigen identification starting from the genome: a "Reverse Vaccinology" approach applied to MenB.

Palumbo E, Fiaschi L, Brunelli B, Marchi S, Savino S, Pizza M.

Methods Mol Biol. 2012;799:361-403. doi: 10.1007/978-1-61779-346-2_21.

PMID:
21993656
11.

Pseudomonas aeruginosa antigens as potential vaccines.

Stanislavsky ES, Lam JS.

FEMS Microbiol Rev. 1997 Nov;21(3):243-77. Review.

12.

Support vector machine-based quantitative structure-activity relationship study of cholesteryl ester transfer protein inhibitors.

Riahi S, Pourbasheer E, Ganjali MR, Norouzi P.

Chem Biol Drug Des. 2009 May;73(5):558-71. doi: 10.1111/j.1747-0285.2009.00800.x.

PMID:
19323654
13.

Selection of Vaccine Candidates for Fish Pasteurellosis Using Reverse Vaccinology and an In Vitro Screening Approach.

Andreoni F, Amagliani G, Magnani M.

Methods Mol Biol. 2016;1404:181-92. doi: 10.1007/978-1-4939-3389-1_12.

PMID:
27076298
14.

Mature Epitope Density--a strategy for target selection based on immunoinformatics and exported prokaryotic proteins.

Santos AR, Pereira VB, Barbosa E, Baumbach J, Pauling J, Röttger R, Turk MZ, Silva A, Miyoshi A, Azevedo V.

BMC Genomics. 2013;14 Suppl 6:S4. doi: 10.1186/1471-2164-14-S6-S4.

15.

Why is a protective antigen protective?

Gourlay LJ, Colombo G, Soriani M, Grandi G, Daura X, Bolognesi M.

Hum Vaccin. 2009 Dec;5(12):872-5. Review.

PMID:
19786841
17.

Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods.

Lv W, Xue Y.

Eur J Med Chem. 2010 Mar;45(3):1167-72. doi: 10.1016/j.ejmech.2009.12.038.

PMID:
20053484
18.

Protein remote homology detection based on auto-cross covariance transformation.

Liu X, Zhao L, Dong Q.

Comput Biol Med. 2011 Aug;41(8):640-7. doi: 10.1016/j.compbiomed.2011.05.015.

PMID:
21664609
19.

P-glycoprotein substrate models using support vector machines based on a comprehensive data set.

Wang Z, Chen Y, Liang H, Bender A, Glen RC, Yan A.

J Chem Inf Model. 2011 Jun 27;51(6):1447-56. doi: 10.1021/ci2001583.

PMID:
21604677
20.

Antigenome technology: a novel approach for the selection of bacterial vaccine candidate antigens.

Meinke A, Henics T, Hanner M, Minh DB, Nagy E.

Vaccine. 2005 Mar 18;23(17-18):2035-41.

PMID:
15755567
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