H.3.2.10. Medicine and science
Ali Ghanbari; Mohaddeseh Keyhanian; Jamshid pirgazi
Abstract
Accurate prediction of drug–target interactions is essential for advancing drug discovery and repositioning efforts. This study introduces a comprehensive framework that effectively addresses key challenges in DTI prediction, including dataset imbalance and high-dimensional feature representations. ...
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Accurate prediction of drug–target interactions is essential for advancing drug discovery and repositioning efforts. This study introduces a comprehensive framework that effectively addresses key challenges in DTI prediction, including dataset imbalance and high-dimensional feature representations. The approach integrates multiple protein descriptors—specifically, nine statistical and sequence-based features—and drug molecular fingerprints encoded via Morgan algorithms, with optimal feature combinations selected through validation to capture diverse biological and chemical information. To mitigate dataset imbalance, a one-class SVM-based undersampling method (One-SVM-US) models the distribution of positive interactions to guide the selective reduction of the majority class, thereby effectively balancing positive and negative samples. Furthermore, a supervised, classification-oriented variational autoencoder is employed to compress the high-dimensional features into a lower-dimensional space while preserving class-discriminative information relevant to interaction prediction. The refined features are then classified using machine learning models to predict potential drug–target pairs. Experimental evaluations on benchmark datasets demonstrate the effectiveness of the proposed framework, with results showing perfect AUC-ROC scores of 1.00 on the EN, GPCR, and NR datasets, and a score of 0.9731 on the IC dataset, indicating performance improvements over existing methods. These findings confirm the robustness and potential of the approach as a reliable tool for drug–target interaction prediction.
H.3. Artificial Intelligence
Elahe Moradi
Abstract
Liver disorders are among the most common diseases worldwide, and their timely diagnosis and prediction can significantly improve treatment outcomes. In recent years, the application of artificial intelligence, particularly machine learning and deep learning algorithms, in the medical field has gained ...
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Liver disorders are among the most common diseases worldwide, and their timely diagnosis and prediction can significantly improve treatment outcomes. In recent years, the application of artificial intelligence, particularly machine learning and deep learning algorithms, in the medical field has gained tremendous importance and has led to reduced healthcare costs. In this study, the ILPD dataset from the UCI Machine Learning Repository, which comprises 583 liver patient records with 11 features, was utilized. In this research, a predictive framework based on Multilayer Perceptron (MLP) is employed for the prediction of liver disorders. To address the class imbalance in the binary classification dataset, the Synthetic Minority Oversampling Technique (SMOTE)–Tomek approach was implemented to improve data balance. Moreover, due to the presence of a substantial number of outlier values, a robust scaling method was applied for their management. Finally, the performance of the proposed method was compared with three well-known machine learning algorithms. To enhance evaluation robustness, a five-fold cross-validation was employed across all classifiers. All simulations were conducted using Python, and the results illustrate that the proposed method achieves superior performance, with an accuracy of 90.90% compared to state-of-the-art approaches.
