تالیفات

 

Consensus classification criteria for paediatric Behçet’s disease from a prospective observational cohort: PEDBD

Authers: Isabelle Koné-Paut, 1 Fahrad Shahram, 2 Martha Darce-Bello, 1 Luca Cantarini, 3 Rolando Cimaz, 4 Marco Gattorno, 5 Jordi Anton, 6 Michael Hofer, 7 Bouchra Chkirate, 8 Kenza Bouayed, 9 Ilknur Tugal-Tutkun, 10 Jasmin Kuemmerle-Deschner, 11 Hélène Agostini,

Abstract: We aimed to describe the main features of Behçet’s disease (BD) in children in the largest prospective cohort to date and to propose a classification. Methods An international expert consensus group was formed to define a data set of minimal symptoms for the inclusion of patients. Patients were entered prospectively during 66 months. Experts classified patients on a consensus basis. The concordance of two international classifications was analysed in confirmed patients with BD. Comparisons of subgroups of patients helped define consensus ... سال انتشار: 2015
 

Random Forests Analysis: A modern statistical method for screening in high-dimensional studies and its application in a population-based genetic association study

Authers: Sahar Noori1, Keramat Nourijelyani 2*, Kazem Mohammad3, Mohammad Hossein Niknam4, Mahdi Mahmoudi5, Laris Andonian6, and Arash Akaberi7

Abstract: Technology advances in this century, especially, in molecular generics yields high volume, high dimensional data. This creates many unprecedented challenges for statisticians who are responsible for analysis of such data. Although logistic regression method is quite popular in association analysis in medical researches but it has some serious limitations in handling high dimensional data. In present study, our goal is introduce a modern model-free statistical method called random forest that we believe is able to overcome difficulties ... سال انتشار: 2012
 

Random Forests Analysis: A modern statistical method for screening in high-dimensional studies and its application in a population-based genetic association study

Authers: Sahar Noori, Keramat Nourijelyani, Kazem Mohammad, Mohammad Hossein Niknam, Mahdi Mahmoudi, Laris Andonian, Arash Akaberi

Abstract: Technology advances in this century, especially, in molecular generics yields high volume, high dimensional data. This creates many unprecedented challenges for statisticians who are responsible for analysis of such data. Although logistic regression method is quite popular in association analysis in medical researches but it has some serious limitations in handling high dimensional data. In present study, our goal is introduce a modern model-free statistical method called random forest that we believe is able to overcome difficulties ... سال انتشار: 2011
 

Analysis Of Microsatellite Polymorphism Around The HLA-B Locus In Iranian Patients With Behcet's Disease.

Authers: N. Mizuki, K. Yabuki, M. Ota, Y. Katsuyama, H. Ando, E. Nomura, K. Funakoshi, F. Davatchi, H. Chams, B. Nikbin, A.A. Ghaderi, S. Ohno, H. Inoko H

Abstract: We have previously suggested that in a Japanese population the susceptible locus for Behcet's disease (BD) is HLA-B51 itself. To confirm this finding in another population, we performed HLA class I typing using the PCR-SSP method and analyzed eight polymorphic markers distributed within 1100 kb around the HLA-B gene using automated sequencer and subsequent automated fragment detection by fluorescent-based technology with the DNA samples of 84 Iranian patients with BD ... سال انتشار: 2002