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All Journal International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Ghina Khoerunnisa
Universitas Telkom
Computer Science & IT Engineering

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Prediction of Retweets Based on User, Content, and Time Features Using EUSBoost Ghina Khoerunnisa; Jondri; Widi Astuti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.217 KB) | DOI: 10.29207/resti.v6i3.4125

Abstract

Twitter is one of the popular microblogs that allow users to write posts. Retweeting is one of the mechanisms for the diffusion of information on Twitter. One way to understand the spread of information is to learn about retweet predictions. This study focuses on predicting retweets using Evolutionary Undersampling Boosting (EUSBoost) based on user, content, and time-based features. We also consider the vector of text as a predictive feature. Models with EUSBoost are able to outperform models using the AdaBoost method. The evaluation results show that the best model can achieve an AUC performance score of 77.21% and a GM score of 77.18%. While the Adaboost-based models achieved AUC scores ranging from 68% to 69% and GM scores ranging from 62% to 63%. In addition, we found that there was no significant difference between using numeric features only and combining numeric and text features.
Enhancing drug-target affinity prediction through pre-trained language model and gated multi-head attention Khoerunnisa, Ghina; Kurniawan, Isman
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1910

Abstract

Drug development requires accurate drug-target interaction (DTI) information to evaluate a drug's potential. However, existing current methods for estimating DTI are slow and expensive. Deep learning offers an efficient and effective alternative by leveraging sequence data for prediction. Nevertheless, the DTI binary classification approach suffers from a large number of non-interacting pairs, resulting in data imbalance and has a negative impact on performance. To address this issue, DTI is modeled as a regression problem known as drug-target affinity (DTA), which predicts the strength of interactions. While various deep learning methods show competitive results in DTA prediction, they face a challenge in capturing specific drug-target patterns with limited data. To overcome the problem, this study leverages pre-trained language models for enhanced representation. Also, we utilize gated multi-head attention (GMHA), which modifies multi-head attention by including dynamic scaling and a gate process to capture the mutual interactions better. The results show that our proposed method exceeds the benchmark and baseline in all evaluation metrics, with concordance index (CI) of 0.893 and 0.872, and modified r-squared (rm2) of 0.673 and 0.723 in Davis and KIBA. Our findings further suggest that pre-trained language models for drug and target receptor representation improve DTA prediction model performance. Also, the GMHA method generally outperforms the simple concatenation method, with more obvious advantages in more complex datasets like KIBA. Our approach provides a competitive enhancement in DTA prediction, suggesting a promising direction for further enhancing drug discovery and development processes.
Co-Authors Isman Kurniawan Jondri Widi Astuti