Please note: This PhD seminar will take place online.
Shaokai Wang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Bin Ma
Amyloid light chain (AL) amyloidosis is a disorder characterized by the deposition of antibody light chains in organs. The importance of early and accurate diagnosis in AL amyloidosis cannot be overstated, as it enables timely implementation of appropriate treatment strategies and improves patient outcomes. Therefore, developing a highly accurate method using antibody sequencing and computational techniques is crucial to address this urgent need. While several computational methods have been developed to predict AL amyloidosis, they heavily depend on manually extracted features, and their performance falls short of satisfactory levels.
In this paper, we developed DeepAL, a deep learning-based approach to predict AL amyloidosis with high precision. DeepAL utilizes a pre-trained model to extract light chain features from and then trained with AL amyloidosis knowledge. On two benchmark datasets, DeepAL achieved 90.72% and 89.2% in terms of the area under the ROC curves (AUCs) respectively, outperforming previous approaches. In our ablation study, we show the pre-trained model increases 11.5% AUC.