Volume 31, Issue 10 (1-2024)
JSSU 2024, 31(10): 7108-7116 |
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Ethics code: IR.MUI.RESEARCH.REC.1400.322
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Arabi N, Torabi M R, Fassihi A, Ghasemi F. Finding New VEGFR2 Inhibitors Using Support Vector Machine Classification Model. JSSU 2024; 31 (10) :7108-7116
URL: http://jssu.ssu.ac.ir/article-1-5979-en.html
URL: http://jssu.ssu.ac.ir/article-1-5979-en.html
Abstract: (188 Views)
Introduction: In our current era, the prevalence of cancer and its associated mortality rates have become a pressing concern. As such, finding effective methods for treating cancer has become a matter of significant importance. Abnormal angiogenesis is one of the common characteristics of different types of cancer. So far, the inhibition of vascular endothelial growth factor receptor 2 signaling pathway has received much attention due to its pro-angiogenic role. Therefore, finding reliable computational models to identify inhibitors can be effective in reducing time and cost. The purpose of this study was to use the support vector machine method to classify compounds into two inhibitory and non-inhibitory groups.
Methods: In order to implement the machine learning model, the ligands studied in this research were extracted from the https://www.bindingdb.org database and after passing the necessary pre-processing, some filter-based and embedded feature selection methods were used. After extracting the descriptors from the data, using the feature selection algorithm based on correlation, the dimensions of the data have been reduced in order to avoid overfitting the model. The classification task utilized a support vector machine model, employing various kernels such as Radial Basis Function (RBF), Polynomial, Sigmoid, and Linear.
Results: The implementation of the support vector machine model with the RBF kernel along with the feature selection method based on correlation has resulted in a higher accuracy of 82.4% (P=0.008) compared to other feature selection methods used in this study.
Conclusion: Observations indicate that the correlation-based feature selection method is more accurate than other methods used in this study.
Methods: In order to implement the machine learning model, the ligands studied in this research were extracted from the https://www.bindingdb.org database and after passing the necessary pre-processing, some filter-based and embedded feature selection methods were used. After extracting the descriptors from the data, using the feature selection algorithm based on correlation, the dimensions of the data have been reduced in order to avoid overfitting the model. The classification task utilized a support vector machine model, employing various kernels such as Radial Basis Function (RBF), Polynomial, Sigmoid, and Linear.
Results: The implementation of the support vector machine model with the RBF kernel along with the feature selection method based on correlation has resulted in a higher accuracy of 82.4% (P=0.008) compared to other feature selection methods used in this study.
Conclusion: Observations indicate that the correlation-based feature selection method is more accurate than other methods used in this study.
Keywords: Vascular Endothelial Growth Factor Receptor II, Quantitative Structure-Activity Relationship, Support Vector Machine, Angiogenesis.
Type of Study: Original article |
Subject:
other
Received: 2023/04/18 | Accepted: 2023/10/1 | Published: 2024/01/5
Received: 2023/04/18 | Accepted: 2023/10/1 | Published: 2024/01/5
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