Problem Solving Protocol: Accurate Residue-Level Phase Separation Prediction Using Protein Conformational and Language Model Embeddings

Life Sciences, Biochemistry, Genetics, Biophysics,
phase separation   language model   residue-level prediction   conformational embeddings   intrinsically disordered regions  

1. Academic Background and Research Significance In recent years, protein liquid–liquid phase separation (PS) has emerged as a key mechanism regulating biomolecules inside cells, attracting widespread attention in the life sciences. Phase separation not only drives the formation of biomolecular condensates (membraneless organelles), but also extens...

Chrombus-XMBD: A Graph Convolution Model Predicting 3D-Genome from Chromatin Features

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Cell Biology, Genetics, Biophysics,
3D-genome   graph convolution model   chromatin features   long-range interaction   model generalizability  

Research Background and Disciplinary Significance In eukaryotic cells, the three-dimensional (3D) spatial structure of chromatin plays a crucial role in gene expression regulation. Through complex folding, looping, and local spatial reconfiguration of DNA, different genetic elements (such as promoters and enhancers) are brought into spatial proximi...

Deep scSTAR: Leveraging Deep Learning for the Extraction and Enhancement of Phenotype-Associated Features from Single-Cell RNA Sequencing and Spatial Transcriptomics Data

Information Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Immunology,
Single-cell RNA sequencing   Spatial transcriptomics   Deep learning   Phenotype-associated features   Tumor microenvironment  

In recent years, cutting-edge technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have greatly advanced the development of life sciences and clinical medicine. These technologies have revealed cellular heterogeneity and brought novel insights into major fields such as disease, development, and immunity. Howe...

Ensemble Learning Based on Matrix Completion Improves Microbe-Disease Association Prediction

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Microbiology,
microbe-disease association   ensemble learning   matrix completion   prediction algorithm   bioinformatics  

Academic Background and Research Problem Microorganisms, as one of the most widely distributed forms of life on Earth, are closely related to oceans, soil, and the human body. The human body contains approximately 350 trillion microbial cells, which are intricately linked to human health and the onset and progression of diseases. In recent years, w...

Benchmarking Copy Number Aberrations Inference Tools Using Single-Cell Multi-Omics Datasets

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Cell Biology, Bioengineering, Genetics,
Single-cell RNA sequencing   Copy number aberrations   Tumor heterogeneity   Benchmarking   Multi-omics  

1. Research Background and Significance In the fields of oncology and genomics, chromosomal copy number alterations (CNAs) are a key type of genetic variation driving the occurrence and progression of cancer. CNAs not only determine tumor heterogeneity but also play a crucial role in early tumor detection, subclone evolution analysis, research on d...