In SilicoPrediction of Blood–Brain Partitioning Using a Chemometric Method Called Genetic Algorithm Based Variable Selection

2008 ◽  
Vol 27 (6) ◽  
pp. 704-717 ◽  
Author(s):  
Jie Shen ◽  
Yiping Du ◽  
Yaxue Zhao ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Chemometric Method ◽  
Blood Brain

Development of a Genetic Algorithm to Design and Identify Peptides that can Cross the Blood-Brain Barrier

2003 ◽  
Vol 22 (7) ◽  
pp. 745-753 ◽  
Author(s):  
Meritxell Teixidó ◽  
Ignasi Belda ◽  
Xavier Roselló ◽  
Sonia González ◽  
Myriam Fabre ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Blood Brain

scholarly journals Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

2019 ◽  
pp. 1-15 ◽  
Author(s):  
Leila Emami ◽  
Razieh Sabet ◽  
Amirhossein Sakhteman ◽  
Mehdi Khoshnevis Zade
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Dipeptidyl Peptidase ◽  
Docking Studies ◽  
Quantitative Structure ◽  
Dipeptidyl Peptidase 4 ◽  
Structure Activity

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme.  DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

scholarly journals Penerapan Algoritme Genetik Untuk Seleksi Peubah Regresi Logistik

2020 ◽  
Vol 9 (1) ◽  
pp. 51-58
Author(s):  
Dian Ayuningtyas ◽  
Bagus Sartono ◽  
Farit Mochamad Afendi
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Optimization Method ◽  
Response Variable ◽  
Absolute Value ◽  
Independent Variables ◽  
Interaction Factors ◽  
Independent Variable ◽  
Positive Effect ◽  
Excessive Number

In a study, interaction factors are the potential to have important effects on the response variable. But research involving interaction factors often encounters two problems, namely the excessive number of variables and the difficulty of implementing the heredity principle. The alternative solution is to do variable selection using a metaheuristic optimization method, In this study, the logistic regression variable selection was done using a genetic algorithm. The genetic algorithm is modified so that every independent variable has a different probability to be included in the model. That probability is based on the absolute value of the correlation of the independent variable with the response variable. These modifications have a positive effect on the results of variable selection. To choose significant independent variables, 30 repetitions of the genetic algorithm can be performed using the objective function AIC. Of the 30 repetitions, if a variable appears in all formed models, then the variable is an independent variable that has a significant effect on the response variable. The application of this method to Myopia data can show significant variables well.

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