2025/07/24 by Mintian Cui, Shixi Wang, Fan Yang +10 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #Adipokines, Inflammation, and Metabolic Diseases #Dermatology and Skin Diseases #Single-cell and spatial transcriptomics
paper · doi:10.1101/2025.07.21.665890
openalex publication_date 2025/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract Clinical and biological insights from large-scale transcriptomic data are often limited by technical variability and analytical complexity. Here, we introduce BioinAI, a general bioinformatic framework designed for multi-level transcriptomic data analysis. As part of the BioinAI framework, two algorithms named DeepAdvancer and stNiche were developed to improve data integration and analytical efficiency. DeepAdvancer leverages a class-aware adversarial autoencoder to reconstruct gene-expression profiles from 49,738 samples across 131 skin conditions. These profiles revealed a conserved inflammatory axis and a transcriptomic continuum which link diverse diseases through shared immune responses and structural programs. stNiche leverages spatial graph networks and symmetry-aware matching to identify functional cellular niches and reveals microstructural alterations across development, homeostasis, and disease. For instance, it identified a fibroblast–immune niche surrounding hair follicles in healthy skin, which disappears in pathological states. In addition, BioinAI provides a user-friendly online analysis platform powered by multiple semi-agents ( www.bioinai.com ), significantly facilitating the extraction of biological insights to advance scientific research.