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bioRxiv · 10.1101/2025.07.21.665890

BioinAI: a general bioinformatic framework for multi-level transcriptomic data analysis using multiple semi-agents

Cui, M.·Wang, S.·Yang, F.·Wang, Y.·Kong, F.·Kong, N.·Li, M.·Qiao, X.·Xu, Z.·Yan, Z.·Yan, Y.·Zhang, J.·Chen, K.
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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.

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Cui, M., Wang, S., Yang, F., Wang, Y., Kong, F., Kong, N., Li, M., Qiao, X., Xu, Z., Yan, Z., Yan, Y., Zhang, J., Chen, K.. 2025-07-24. BioinAI: a general bioinformatic framework for multi-level transcriptomic data analysis using multiple semi-agents. https://doi.org/10.1101/2025.07.21.665890

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