Orhun Vural, PhD

All Publications

• DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model. IEEE transactions on computational biology and bioinformatics Vural, O., Jololian, L., Pan, L. 2025; 22 (1): 116-123

  Abstract

  The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making in drug design. Our study, DeepLigType, determines protein-ligand binding sites using Fpocket and then predicts the ligand type of these pockets with the deep learning model, Convolutional Block Attention Module (CBAM) with ResNet. CBAM-ResNet has been trained to accurately predict five distinct ligand types. We classified protein-ligand binding sites into five different categories according to the type of response ligands cause when they bind to their target proteins, which are antagonist, agonist, activator, inhibitor, and others. We created a novel dataset, referred to as LigType5, from the widely recognized PDBbind and scPDB dataset for training and testing our model. While the literature mostly focuses on the specificity and characteristic analysis of protein binding sites by experimental (laboratory-based) methods, we propose a computational method with the DeepLigType architecture. DeepLigType demonstrated an accuracy of 74.30% and an AUC of 0.83 in ligand type prediction on a novel test dataset using the CBAM-ResNet deep learning model.

  View details for DOI 10.1109/TCBB.2024.3493820

  View details for PubMedID 39509302

• Assessing the Impact of External and Internal Factors on Emergency Department Overcrowding HEALTHCARE Ahmed, A., Aram, K. Y., Alzeen, M., Vural, O., Booth, J., Lindsey, B. F., Ozaydin, B. 2025; 13 (20)

  View details for DOI 10.3390/healthcare13202577

  View details for Web of Science ID 001601611200001

• An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study. JMIR medical informatics Vural, O., Ozaydin, B., Aram, K. Y., Booth, J., Lindsey, B. F., Ahmed, A. 2025; 13: e73960

  Abstract

  Emergency department (ED) overcrowding remains a critical challenge, leading to delays in patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion only after it occurs. However, effective patient flow management requires early identification of overcrowding risks to allow timely interventions. Machine learning (ML)-based predictive modeling offers a solution by forecasting key patient flow measures, such as waiting count, enabling proactive resource allocation and improved hospital efficiency.The aim of this study is to develop ML models that predict ED waiting room occupancy (waiting count) at 2 temporal resolutions. The first approach is the hourly prediction model, which estimates the waiting count exactly 6 hours ahead at each prediction time (eg, a 1 PM prediction forecasts 7 PM). The second approach is the daily prediction model, which forecasts the average waiting count for the next 24-hour period (eg, a 5 PM prediction estimates the following day's average). These predictive tools support resource allocation and help mitigate overcrowding by enabling proactive interventions before congestion occurs.Data from a partner hospital's ED in the southeastern United States were used, integrating internal and external sources. Eleven different ML algorithms, ranging from traditional approaches to deep learning architectures, were systematically trained and evaluated on both hourly and daily predictions to determine the models that achieved the lowest prediction error. Experiments optimized feature combinations, and the best models were tested under high patient volume and across different hours to assess temporal accuracy.The best hourly prediction performance was achieved by time series vision transformer plus (TSiTPlus) with a mean absolute error (MAE) of 4.19 and a mean squared error (MSE) of 29.36. The overall hourly waiting count had a mean of 18.11 and a SD (σ) of 9.77. Prediction accuracy varied by time of day, with the lowest MAE at 11 PM (2.45) and the highest at 8 PM (5.45). Extreme case analysis at (mean + 1σ), (mean + 2σ), and (mean + 3σ) resulted in MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, an explainable convolutional neural network plus (XCMPlus) achieved the best results with an MAE of 2.00 and a MSE of 6.64. The daily waiting count had a mean of 18.11 and a SD of 4.51. Both models outperformed traditional forecasting approaches across multiple evaluation metrics.The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository.

  View details for DOI 10.2196/73960

  View details for PubMedID 40961493

  View details for PubMedCentralID PMC12489414

• Deep Learning-Based Forecasting of Boarding Patient Counts to Address Emergency Department Overcrowding INFORMATICS-BASEL Vural, O., Ozaydin, B., Booth, J., Lindsey, B. F., Ahmed, A. 2025; 12 (3)

  View details for DOI 10.3390/informatics12030095

  View details for Web of Science ID 001580012500001

• Machine learning approaches for predicting protein-ligand binding sites from sequence data FRONTIERS IN BIOINFORMATICS Vural, O., Jololian, L. 2025; 5

  View details for DOI 10.3389/fbinf.2025.1520382

  View details for Web of Science ID 001422292400001