Toward computational modelling on immune system function.
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
14 Dec 2020
14 Dec 2020
Historique:
entrez:
14
12
2020
pubmed:
15
12
2020
medline:
5
1
2021
Statut:
epublish
Résumé
The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.
Identifiants
pubmed: 33308137
doi: 10.1186/s12859-020-03897-5
pii: 10.1186/s12859-020-03897-5
pmc: PMC7733695
doi:
Types de publication
Editorial
Langues
eng
Sous-ensembles de citation
IM
Pagination
546Références
Russo G, Pennisi M, Fichera E, Motta S, Raciti G, Viceconti M, Pappalardo F. In silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03872-0 .
doi: 10.1186/s12859-020-03872-0
Russo G, Sgroi G, Giuseppe Alessandro PP, Pennisi M, Juarez M, Cardona P-J, Motta S, Walker KB, Fichera E, Viceconti M, Pappalardo F. Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03762-5 .
doi: 10.1186/s12859-020-03762-5
Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03763-4 .
doi: 10.1186/s12859-020-03763-4
Gomez-Perosanz M, Ras-Carmona A, Lafuente EM, Reche P. Identification of CD8+ T cell epitopes through proteasome cleavage site predictions. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03782-1 .
doi: 10.1186/s12859-020-03782-1
Zhang P, Roberts T, Richards B, Haseler LJ. Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03814-w .
doi: 10.1186/s12859-020-03814-w
Bonin CR, Fernandes GC, Menezes RM, AB, CL, Teixeira-Carvalho A, da Mota LMH, de Lima SMB, Campi-Azevedo AC, Martins-Filho OA, dos Santos R, Lobosco M. . Validation of a yellow fever vaccine model using data from primary vaccination in children and adults, re-vaccination and dose-response in adults and studies with immunocompromised individuals. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03845-3 .
Roither B, Oostenbrink C, Schreiner W. Molecular dynamics of the immune checkpoint programmed cell death protein I, PD- 1: conformational changes of the BC-loop upon binding of the ligand PD-L1 and the monoclonal antibody nivolumab. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03904-9 .
doi: 10.1186/s12859-020-03904-9
Pernice S, Maglione A, Pennisi M, Pappalardo F, Novelli F, Clerico M, Beccuti M, Cordero F, Rolla S. Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03823-9 .
doi: 10.1186/s12859-020-03823-9
Juarez M, Pennisi M, Russo G, Kiagias D, Curreli C, Viceconti M, Pappalardo F. Generation of digital patients for the simulation of tuberculosis with UISS-TB. BMC Bioinform. 2020. https://doi.org/10.1186/s12859-020-03776-z .
doi: 10.1186/s12859-020-03776-z