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A Comparison of Logistic Regression and Linear Discriminant Analysis in the Understanding of Gene Regulatory Response

المصدر: المجلة العلمية للاقتصاد والتجارة
الناشر: جامعة عين شمس - كلية التجارة
المؤلف الرئيسي: Etman, Nihal Aly (Author)
مؤلفين آخرين: Aal, Medhat Adel (Advisor) , Abd El Alim, Mamdouh (Advisor)
المجلد/العدد: ع4
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2021
الشهر: ديسمبر
الصفحات: 605 - 634
ISSN: 2636-2562
رقم MD: 1206799
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
كلمات المؤلف المفتاحية:
Gene Expression Regulation | Micro RNA | mRNA | Seed Match | Free Energy | Linear Discriminant Analysis (LDA) | Logistic Regression (LR)
رابط المحتوى:
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LEADER 02741nam a22002417a 4500
001 1953798
041 |a eng 
044 |b مصر 
100 |9 645759  |a Etman, Nihal Aly  |e Author 
245 |a A Comparison of Logistic Regression and Linear Discriminant Analysis in the Understanding of Gene Regulatory Response 
260 |b جامعة عين شمس - كلية التجارة  |c 2021  |g ديسمبر 
300 |a 605 - 634 
336 |a بحوث ومقالات  |b Article 
520 |b Gene expression regulation is a vital process in the body to ensure that cells produce the correct amount of proteins when they need them. Any disruption to this regulation can lead to serious consequences, including cancer). miRNAs are micro molecules that control gene expression by targeting a mRNA and binding to specific sites within the 3'UTR or the 5'UTR and increase or decrease gene expression. Hence, it's crucial to predict gene regulatory response in order to be able to control it. Two of the most widely used statistical methods for analyzing categorical outcome variables are LDA and logistic regression. While both are appropriate for the development of linear classification models, i.e. models associated with linear boundaries between the groups. Nevertheless, the two methods differ in their basic idea. LDA makes more assumptions about the underlying data. It is therefore reasonable to expect LDA to give better results in the case when the normality assumptions are fulfilled, but in all other situations LR should be more appropriate. However, in practice, the assumptions are nearly always violated; therefore, we try to check the performance of both methods with simulations. Previously (In our last paper) we have studied gene regulatory mechanisms using Logistic Regression. In this paper, we present a simulation study between Logistic Regression and LDA in the prediction of gene regulatory response. 
653 |a التعبير الجينى  |a تنظيم التعبير الجينى  |a الاستجابة التنظيمية للجينات  |a النماذج الإحصائية  |a الانحدار اللوجستي  |a تحليل التمييز الخطي 
692 |b Gene Expression Regulation  |b Micro RNA  |b mRNA  |b Seed Match  |b Free Energy  |b Linear Discriminant Analysis (LDA)  |b Logistic Regression (LR) 
700 |9 645763  |a Aal, Medhat Adel  |e Advisor 
700 |9 645765  |a Abd El Alim, Mamdouh  |e Advisor 
773 |4 الاقتصاد  |6 Economics  |c 024  |e Scientific Journal for Economic & Commerce  |f Al-Maġallah Al-ʿilmiyyah Lil-Iqtiṣād Wal Tiğārah  |l 004  |m ع4  |o 0527  |s المجلة العلمية للاقتصاد والتجارة  |v 051  |x 2636-2562 
856 |u 0527-051-004-024.pdf 
930 |d y  |p y  |q n 
995 |a EcoLink 
999 |c 1206799  |d 1206799 

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