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...

HSSPPI: Hierarchical and Spatial-Sequential Modeling for PPIs Prediction

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering, Genetics,
protein–protein interaction sites   hierarchical graph   spatial form   sequential form   feature fusion  

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

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Bioengineering,
spatial transcriptomics   spatial domain identification   joint domain detection   graph masked autoencoder   graph contrastive learning  

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...

TopoQA: A Topological Deep Learning-Based Approach for Protein Complex Structure Interface Quality Assessment

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering, Biophysics,
Quality Assessment   Protein Interface   Persistent Homology   Graph Neural Network   Topological Data  

Academic Background The elucidation of protein complex 3D structures is a central topic in modern structural biology, molecular mechanism studies, drug design, and even artificial protein design. The function of a protein is often determined by its structure, and many biological processes involve complex interactions between proteins. Although trad...

Inferring Gene Regulatory Networks from Time-Series scRNA-Seq Data via Granger Causal Recurrent Autoencoders

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Cell Biology, Genetics,
scRNA-seq   gene regulatory network   unsupervised learning   Granger causality   recurrent variational autoencoder  

1. Academic Background and Research Motivation In recent years, single-cell RNA sequencing (scRNA-seq) has become one of the most groundbreaking technologies in life sciences and medical research, enabling researchers to capture subtle differences in transcript levels among numerous cells at the resolution of individual cells. This technology has g...

Cox-SAGE: Enhancing Cox Proportional Hazards Model with Interpretable Graph Neural Networks for Cancer Prognosis

Information Science, Computer Science, Artificial Intelligence, Medicine, Hepatobiliary System, Oncology,
Cox proportional hazards model   cancer prognosis   graph neural networks   biomarker discovery   patient similarity graph  

1. Research Background and Disciplinary Frontiers Cancer prognosis analysis has always been a core research direction in the medical field. In recent years, with the widespread application of high-throughput sequencing technologies, scientists have been able to delve deeper into exploring molecular biomarkers and clinical characteristics of cancer ...