EPICPred: Predicting Phenotypes Driven by Epitope-Binding TCRs Using Attention-Based Multiple Instance Learning

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Bioengineering, Immunology,
epitope-binding TCRs   multiple instance learning   phenotype prediction   cancer   COVID-19  

T-cell receptors (TCRs) play a crucial role in the adaptive immune system by recognizing pathogens through binding to specific antigen epitopes. Understanding the interactions between TCRs and epitopes is essential for uncovering the biological mechanisms of immune responses and developing T cell-mediated immunotherapies. However, although the impo...

SCICONE: Single-Cell Copy Number Calling and Event History Reconstruction

Information Science, Computer Science, Medicine, Oncology, Life Sciences, Cell Biology, Bioengineering, Genetics,
single-cell sequencing   copy number variation   tumor evolution   SCICONE model   low-depth sequencing  

During tumor development, copy number alterations (CNAs) are key drivers of tumor heterogeneity and evolution. Understanding these variations is crucial for developing personalized cancer diagnostics and therapies. Single-cell sequencing technology offers the highest resolution for copy number analysis, down to the individual cell level. However, l...

ImmunoTar—Integrative Prioritization of Cell Surface Targets for Cancer Immunotherapy

Information Science, Computer Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Immunology,
cancer immunotherapy   cell surface targets   RNA sequencing   proteomics   computational tool  

Cancer remains one of the leading causes of death globally. Despite significant advancements in immunotherapy in recent years, such as the successful application of chimeric antigen receptor T-cell (CAR-T) therapy and antibody-drug conjugates (ADCs), the effective identification of cancer-specific surface protein targets remains a major challenge i...

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...

Contrastive Mapping Learning for Spatial Reconstruction of Single-Cell RNA Sequencing Data

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Bioengineering,
Single-cell RNA sequencing   Spatial transcriptomics   Contrastive learning   Cell correspondence   Gene expression  

Single-cell RNA sequencing (scRNA-seq) technology enables high-throughput transcriptomic profiling at single-cell resolution, significantly advancing research in cell biology. However, a notable limitation of scRNA-seq is that it requires tissue dissociation, resulting in the loss of the original spatial location information of cells within tissues...

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 ...