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

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

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

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

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...

HSSPPI: Hierarchical and Spatial-Sequential Modeling for PPIs Prediction

Background: Unveiling the Bottlenecks and Opportunities in Protein Interaction Prediction Proteins serve as the core molecules for life activities, participating in almost all biological processes and cellular functions, including gene expression, RNA transcription, DNA synthesis, immune response, and more. Protein-protein interactions (PPI), as we...

MAEST: Accurate Spatial Domain Detection in Spatial Transcriptomics with Graph Masked Autoencoder

Spatial Transcriptomics: Cutting-Edge Technology for Deciphering Spatial Heterogeneity in Tissues Spatial transcriptomics (ST) is an emerging sequencing technology that has rapidly developed in recent years. Its core advantage lies in the simultaneous acquisition of gene expression and spatial location information at the tissue section level, provi...