Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion models and their applications in computer vision and NLP, there is a lack of an overview of their applications in bioinformatics. This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology. We start with an introduction of the key concepts and theoretical foundations of three cornerstone diffusion modeling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks, and stochastic differential equations), followed by a comprehensive description of diffusion models employed in the different domains of bioinformatics, including cryo-EM data enhancement, single-cell data analysis, protein design and generation, drug and small molecule design, and protein-ligand interaction. The review is concluded with a summary of the potential new development and applications of diffusion models in bioinformatics.
SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
the School of Information Science；Engineering , Yunnan University , 650091 , Kunming, Yunnan , China；College of Nursing Health Sciences , Yunnan Open University , 650599 , Kunming , China
Diffusion-Driven Inertial Generated Data for Smartphone Location Classification
The Hatter Department of Marine Technologies, University of Haifa, Israel（海洋技术系，海法大学，以色列）；The Department of Information Systems, University of Haifa, Israel（信息系统系，海法大学，以色列）
ProDapt: Proprioceptive Adaptation using Long-term Memory Diffusion
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Technical University of Munich (TUM)（慕尼黑工业大学）
The Ohio State University（俄亥俄州立大学）；Indiana University（印第安纳大学）；Fudan University（复旦大学）；Hangzhou City University（杭州城市大学）；The University of Hong Kong（香港大学）；Tongji University（同济大学）；The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）
High-Order Matching for One-Step Shortcut Diffusion Models
Middle Tennessee State University（中田纳西州立大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of New South Wales（新南威尔士大学）；The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Tsinghua University（清华大学）；The Simons Institute for the Theory of Computing at UC Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Anhui University（安徽大学）
Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
City University of Hong Kong（香港城市大学）；Great Bay University（大湾区大学）；National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong（香港中文大学）；Lingnan University（岭南大学）
