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Articles by Duolin Wang
Systems biology approach unveils the cellular and molecular mechanisms of formalin-inactivated whole cell vaccine-induced protective immunity against Coxiella burnetii infection in mice
Abstract Background: The formalin-inactivated Coxiella burnetii virulent phase I vaccine (PIV) has been shown to be more protective than the avirulent phase II vaccine (PIIV) against virulent C. burnetii challenge in animal models. However, the cellular and molecular mechanisms underlying the differential ability of PIV and PIIV to induce protective immunity remain unclear.
ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning
Subcellular localization prediction is crucial for understanding protein functions and cellular processes. Subcellular localization is dependent on tissue and cell lines derived from different cell types. Predicting cell line-specific subcellular localization using the information of protein-protein interactions (PPIs) offers deeper insights into dynamic cellular organization and molecular mechanisms. However, many existing PPI networks contain systematic errors that limit prediction accuracy.
Bridging peptide presentation and T cell recognition with multi-task learning - Nature Machine Intelligence
The immunogenic binding interactions of antigens are complex and interconnected. A new transformer-based model can simultaneously predict the bindings of antigens to two main receptors.
S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure
1 Introduction Proteins play a crucial role in resolving many of the health, energy, and environmental challenges facing society today. An essential task is to gain information about proteins of interest quickly and accurately. Along this line, computational predictions of protein properties from protein sequences play important roles.
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
Abstract This paper proposes a versatile tokenization method and introduces Prot2Token, a model that combines autoregressive language modeling with protein language models (PLMs) to tackle various protein prediction tasks using protein sequences.
S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure
Abstract Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein function and the design of proteins with the desired functions. The prediction and design capacity of PLMs relies on the representation gained from the protein sequences.
S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure
Abstract Large protein language models (PLMs) present excellent potential to reshape protein research by encoding the amino acid sequences into mathematical and biological meaningful embeddings. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily depending on 3D structures.
PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction
Abstract Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) provides a new opportunity for SP prediction, especially for the categories with limited annotated data.
Prediction of Protein Ion-Ligand Binding Sites with ELECTRA
2. Results To benchmark the performance of our method, we compared its predictive performance with existing tools, and selected the Zinc dataset as a case study to understand how its performance is affected by different model configurations. 2.1. Comparison with Other Tools We compared IonPred with six state-of-the-art tools.
Using Artificial Intelligence to Generate Master-Quality Architectural Designs from Text Descriptions
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S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure
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Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images
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