Protein Data
Protein data analysis focuses on extracting meaningful information from protein sequences and structures to understand their functions and interactions. Current research heavily utilizes deep learning, employing architectures like graph neural networks (GNNs), transformers, and recurrent neural networks (RNNs) to predict protein properties, dynamics, and interactions, often incorporating physical properties and evolutionary information. These advancements are crucial for accelerating drug discovery, protein engineering, and our fundamental understanding of biological processes, enabling more efficient and accurate predictions than previously possible.
Papers
A Fusion-Driven Approach of Attention-Based CNN-BiLSTM for Protein Family Classification -- ProFamNet
Bahar Ali, Anwar Shah, Malik Niaz, Musadaq Mansoord, Sami Ullah, Muhammad Adnan
CPE-Pro: A Structure-Sensitive Deep Learning Model for Protein Representation and Origin Evaluation
Wenrui Gou, Wenhui Ge, YangTan, Guisheng Fan, Mingchen Li, Huiqun Yu