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. ...
Read More
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.3.2.10. Medicine and science
Fahimeh Hafezi; Maryam Khodabakhsh
Abstract
Coronavirus disease as a persistent epidemic of acute respiratory syndrome posed a challenge to global healthcare systems. Many people have been forced to stay in their homes due to unprecedented quarantine practices around the world. Since most people used social media during the Coronavirus epidemic, ...
Read More
Coronavirus disease as a persistent epidemic of acute respiratory syndrome posed a challenge to global healthcare systems. Many people have been forced to stay in their homes due to unprecedented quarantine practices around the world. Since most people used social media during the Coronavirus epidemic, analyzing the user-generated social content can provide new insights and be a clue to track changes and their occurrence over time. An active area in this space is the prediction of new infected cases from Coronavirus-generated social content. Identifying the social content that relates to Coronavirus is a challenging task because a significant number of posts contain Coronavirus-related content but do not include hashtags or Corona-related words. Conversely, posts that have the hashtag or the word Corona but are not really related to the meaning of Coronavirus and are mostly promotional. In this paper, we propose a semantic approach based on word embedding techniques to model Corona and then introduce a new feature namely semantic similarity to measure the similarity of a given post to Corona in semantic space. Furthermore, we propose two other features namely fear emotion and hope feeling to identify the Coronavirus-related posts. These features are used as statistical indicators in a regression model to estimate the new infected cases. We evaluate our features on the Persian dataset of Instagram posts, which was collected in the first wave of Coronavirus, and demonstrate that the consideration of the proposed features will lead to improved performance of the Coronavirus incidence rate estimation.
