Mathematical Modeling Using Deep Learning with Applications in Biology and Medicine

Dear Colleagues,

This Special Issue aims to publish original research articles covering advances in mathematical modeling using deep learning (DL) with applications in biology and medicine. There have been recent advances in the applications of machine learning and in particular DL, and techniques in biology and medicine. The increasing collection and integration of large biological and patient data sets over the last decade have witnessed a rise in the application of DL techniques in the fields of biology and medicine. For example, these techniques have been applied in the recent COVID-19 pandemic to help fight against the virus and to omics data to address the problems posed by the complex organization of biological processes in relation to cardiovascular disease. The domain of deep learning is growing in relation to models such as pre-trained language models (PLMs), transformer-based architectures such as vision transformer (ViT), generative adversarial networks (GANs), and explainable artificial intelligence (XAI). Topical areas for deep learning research include causality and relevant and insightful explainability and interpretability for a broader research readership and for clinicians. This Special Issue focuses on the application of DL in biology and medicine. Potential topics include but are not limited to:

• Natural language processing (NLP);
• Deep neural networks, e.g., CNNs, RNNs and their variants;
• Generative adversarial networks (GANs);
• Deep reinforcement learning algorithms;
• Few-shot learning (FLS);
• GAN-based data augmentation;
• Causality in machine learning;
• Explainable artificial intelligence (XAI);
• Explainable deep generative models;
• Self-supervised learning;
• Self/semi-supervised learning;
• Transfer learning;
• Pretrained language models (PLMs) (e.g., transformer and its descendants) and their application in vision.

Dr. Joanna Dipnall
Dr. Lan Du
Guest Editors

Manuscript Submission Information

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