Sul-BERTGRU: An Ensemble Deep Learning Method Integrating Information Entropy-Enhanced BERT and Directional Multi-GRU for S-Sulfhydration Sites Prediction

Information Science, Artificial Intelligence, Life Sciences, Cell Biology, Biochemistry,
deep learning   S-sulfhydration sites   information entropy   BERT   multi-directional GRU   protein modification   bioinformatics  

Background Introduction Post-Translational Modifications (PTMs) are crucial mechanisms for regulating cellular activities, including gene transcription, DNA repair, and protein interactions. Among these, cysteine, a rare amino acid, participates in various PTMs through its thiol group, playing a significant role in redox balance and signal transduc...

Trajectory Alignment of Gene Expression Dynamics

Medicine, Life Sciences, Cell Biology, Biochemistry, Bioengineering, Immunology, Genetics, Microbiology,
gene expression   single-cell transcriptomics   trajectory alignment   bioinformatics   differentially expressed genes  

The advent of single-cell RNA sequencing (scRNA-seq) technology has provided unprecedented resolution for studying gene expression dynamics during cell development and differentiation. However, due to the complexity of biological processes, cell developmental trajectories under different conditions are often asymmetric, posing challenges for data i...

Predicting circRNA–Disease Associations with Shared Units and Multi-Channel Attention Mechanisms

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Bioengineering,
circRNA   disease association   multi-view learning   attention mechanism   contrastive learning   computational model   bioinformatics  

Background Introduction In recent years, circular RNAs (circRNAs), as a novel class of non-coding RNA molecules, have played a significant role in the occurrence, development, and treatment of diseases. Due to their unique circular structure, circRNAs are resistant to degradation by nucleases, making them potential biomarkers and therapeutic target...

APNet: An Explainable Sparse Deep Learning Model to Discover Differentially Active Drivers of Severe COVID-19

Information Science, Artificial Intelligence, Medicine, Infectious Diseases, Life Sciences, Immunology,
COVID-19   deep learning   bioinformatics   proteomics   network biology  

Academic Background The COVID-19 pandemic has had a significant impact on global public health systems. Although the pandemic has somewhat subsided, its complex immunopathological mechanisms, long-term sequelae (such as “long COVID”), and the potential for similar threats in the future continue to drive in-depth research. Severe COVID-19 cases are ...

Mapping the Gene Space at Single-Cell Resolution with Gene Signal Pattern Analysis

Life Sciences, Cell Biology, Bioengineering, Genetics,
single-cell sequencing   gene embedding   graph signal processing   bioinformatics   gene co-expression  

Mapping the Gene Space at Single-Cell Resolution: A Study on Gene Signal Pattern Analysis (GSPA) Academic Background Single-cell RNA sequencing (scRNA-seq) technology has made significant progress in biological research in recent years, particularly in revealing the organizational structure of the cellular state space. However, although many comput...