تالیفات

 

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
 

Association between Endoplasmic Reticulum Aminopeptidase-1 (ERAP-1) and Susceptibility to Ankylosing Spondylitis in Iran

Authers: Mahdi Mahmoudi1, 2, 3, Ahmad Reza Jamshidi2, Ali Akbar Amirzargar1, 3, Elham Farhadi1, Keramat Nourijelyani4, Sasan Fallahi2, 5, Mona Oraei3, Sahar Noori4, and Mohammad Hossein Nicknam1, 3

Abstract: Ankylosing Spondylitis (AS) is an inflammatory arthritis, which affects mainly spine and sacroiliac joints. According to recent studies, ERAP1 is the second most common candidate gene for AS susceptibility after HLA-B27. The aim of this study was to determine the association of ERAP1 gene polymorphisms with AS in Iranian population. The study group comprised 387 Iranian AS patients and 316 healthy controls from Iran. Using Real Time PCR allelic discrimination method, we genotyped four SNPs ... 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