Mattia Prosperi
Mattia Prosperi
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Use of massively parallel ultradeep pyrosequencing to characterize the genetic diversity of hepatitis B virus in drug-resistant and drug-naive patients and to detect minor …
M Solmone, D Vincenti, MCF Prosperi, A Bruselles, G Ippolito, ...
Journal of virology 83 (4), 1718-1726, 2009
Determinants of survival in progressive multifocal leukoencephalopathy
A Marzocchetti, T Tompkins, DB Clifford, RT Gandhi, S Kesari, JR Berger, ...
Neurology 73 (19), 1551-1558, 2009
QuRe: software for viral quasispecies reconstruction from next-generation sequencing data
MCF Prosperi, M Salemi
Bioinformatics 28 (1), 132-133, 2012
Massively parallel pyrosequencing highlights minority variants in the HIV-1 env quasispecies deriving from lymphomonocyte sub-populations
G Rozera, I Abbate, A Bruselles, C Vlassi, G D'Offizi, P Narciso, G Chillemi, ...
Retrovirology 6 (1), 15, 2009
A novel methodology for large-scale phylogeny partition
MCF Prosperi, M Ciccozzi, I Fanti, F Saladini, M Pecorari, V Borghi, ...
Nature communications 2 (1), 1-10, 2011
Predictors of first-line antiretroviral therapy discontinuation due to drug-related adverse events in HIV-infected patients: a retrospective cohort study
MCF Prosperi, M Fabbiani, I Fanti, M Zaccarelli, M Colafigli, A Mondi, ...
BMC infectious diseases 12 (1), 296, 2012
Evolution pathways of IgE responses to grass and mite allergens throughout childhood
A Custovic, HJ Sonntag, IE Buchan, D Belgrave, A Simpson, ...
Journal of Allergy and Clinical Immunology 136 (6), 1645-1652. e8, 2015
Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing
MCF Prosperi, L Prosperi, A Bruselles, I Abbate, G Rozera, D Vincenti, ...
BMC bioinformatics 12 (1), 5, 2011
Selecting anti-HIV therapies based on a variety of genomic and clinical factors
M Rosen-Zvi, A Altmann, M Prosperi, E Aharoni, H Neuvirth, ...
Bioinformatics 24 (13), i399-i406, 2008
Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time
D Frentz, CAB Boucher, M Assel, A De Luca, M Fabbiani, F Incardona, ...
PloS one 5 (7), 2010
Declining prevalence of HIV-1 drug resistance in antiretroviral treatment-exposed individuals in Western Europe
A De Luca, D Dunn, M Zazzi, R Camacho, C Torti, I Fanti, R Kaiser, ...
The Journal of infectious diseases 207 (8), 1216-1220, 2013
Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping
MCF Prosperi, L Bracciale, M Fabbiani, S Di Giambenedetto, F Razzolini, ...
Retrovirology 7 (1), 56, 2010
Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy
A Altmann, M Rosen-Zvi, M Prosperi, E Aharoni, H Neuvirth, E Schülter, ...
PloS one 3 (10), 2008
Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment
MCF Prosperi, A Altmann, M Rosen-Zvi, E Aharoni, G Borgulya, F Bazso, ...
Antivir Ther 14 (3), 433-42, 2009
Factors influencing the normalization of CD4+ T-cell count, percentage and CD4+/CD8+ T-cell ratio in HIV-infected patients on long-term suppressive antiretroviral therapy
C Torti, M Prosperi, D Motta, S Digiambenedetto, F Maggiolo, G Paraninfo, ...
Clinical microbiology and infection 18 (5), 449-458, 2012
Incidence of Malignancies in HIVInfected Patients and Prognostic Role of Current CD4 Cell Count: Evidence from a Large Italian Cohort Study
MCF Prosperi, A Cozzi-Lepri, A Castagna, C Mussini, R Murri, ...
Clinical Infectious Diseases 50 (9), 1316-1321, 2010
Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited settings
RZ Sangeda, F Mosha, M Prosperi, S Aboud, J Vercauteren, RJ Camacho, ...
BMC public health 14 (1), 1035, 2014
Mining Twitter to assess the public perception of the “Internet of Things”
J Bian, K Yoshigoe, A Hicks, J Yuan, Z He, M Xie, Y Guo, M Prosperi, ...
PloS one 11 (7), 2016
Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study)
M Zazzi, R Kaiser, A Sönnerborg, D Struck, A Altmann, M Prosperi, ...
HIV medicine 12 (4), 211-218, 2011
Distinguishing asthma phenotypes using machine learning approaches
R Howard, M Rattray, M Prosperi, A Custovic
Current allergy and asthma reports 15 (7), 38, 2015
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