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.
Cui et al. (Thu,) studied this question.
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