تالیفات

 

Anti-Cyclic Citrullinated Peptide Antibody and Rheumatoid Factor Isotypes in Iranian Patients with Rheumatoid Arthritis: Evaluation of Clinical Value and Association with Disease Activity

Authers: Yadollah Shakiba1, Susan Koopah1, Ahmad Reza Jamshidi2, Ali Akbar Amirzargar1, Ahmad Masoud1, Amir Kiani3, Mohammad Hossein Niknam1, Bahareh Nazari1, and Behrouz Nikbin1, 4

Abstract: In this study we determined the frequency, sensitivity and specificity of anti cyclic citrullinated peptides (anti-CCP) IgG antibody, total rheumatoid factor (RF-T), and RF isotypes in Iranian patients with rheumatoid arthritis (RA) and their association with age, clinical and serological parameters. Anti-CCP and RF-T and RF isotypes level were measured in 418 patients and 399 healthy controls by enzyme-linked immunosurbant assay (ELISA). Additionally, serum C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ... سال انتشار: 2014
 

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