این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Cell Journal، جلد ۲۵، شماره ۳، صفحات ۲۰۳-۲۱۱
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Chronic Obstructive Pulmonary Disease: Novel Genes Detection with Penalized Logistic Regression
چکیده انگلیسی مقاله Objective: This study aimed to introduce novel techniques for identifying the genes associated with developing
chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using regression methods.
Materials and Methods: This is a secondary analysis of the data from an experimental study. We used penalized
logistic regressions with three different types of penalties included least absolute shrinkage and selection operator
(LASSO), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD). The models were
trained using genome-wide expression profiling to define gene networks relevant to the COPD stages. A 10-fold
cross-validation scheme was used to evaluate the performance of the methods. In addition, we validate our
results by the external validity approach. We reported the sensitivity, specificity, and area under curve (AUC) of
the models.
Results: There were 21, 22, and 18 significantly associated genes for LASSO, SCAD, and MCP models, respectively.
The most statistically conservative method (detecting less significant features) was MCP detected 18 genes that were
all detected by the other two approaches. The most appropriate approach was a SCAD penalized logistic regression
(AUC= 96.26, sensitivity= 94.2, specificity= 86.96). In this study, we have a common panel of 18 genes in all three
models that show a significant positive and negative correlation with COPD, in which RNF130, STX6, PLCB1,
CACNA1G, LARP4B, LOC100507634, SLC38A2, and STIM2 showed the odds ratio (OR) more than 1. However, there
was a slight difference between penalized methods.
Conclusion: Regularization solves the serious dimensionality problem in using this kind of regression. More exploration
of how these genes affect the outcome and mechanism is possible more quickly in this manner. The regression-based
approaches we present could apply to overcoming this issue.
کلیدواژه‌های انگلیسی مقاله COPD, Gene expression, LASSO, MCP, Panelized Logistic Regression
نویسندگان مقاله Kimiya Gohari |
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

Anoshirvan Kazemnejad |
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

Shayan Mostafaei |
Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden

Samaneh Saberi |
HPGC Research Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

Ali Sheidaei |
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

نشانی اینترنتی https://www.celljournal.org/article_700011_9d2b8ccd2d3544d0a92e32a82c676c97.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات