application-deep-learning-based-medical-image-segmentation-via

Application of Deep Learning-Based Medical Image Segmentation <em>via</em> Orbital Computed Tomography

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of ...

Hongik University

Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.

Yong Oh Lee

Department of Industrial and Data Engineering

Hongik University

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Yong Oh Lee

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Industrial and Data Engineering

Hongik University

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Department of Industrial and Data Engineering