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An intelligent framework for advancing large-scale omics data integration.

Mintian Cui1, Shixi Wang1, Fan Yang1

  • 1State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200127, China.

Iscience
|May 25, 2026
PubMed
Summary

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Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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This summary is machine-generated.

BioinAI integrates advanced algorithms to analyze large-scale omics data, revealing a transcriptomic continuum linking diseases via immune responses and identifying cellular niches. This framework aids biological insight extraction from complex gene expression profiles.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Large-scale omics data analysis is hindered by technical variability and analytical complexity.
  • Extracting meaningful clinical and biological insights requires advanced integration methods.

Purpose of the Study:

  • To present BioinAI, a comprehensive framework for effective data integration and biological insight extraction from omics data.
  • To overcome limitations in analyzing complex, large-scale transcriptomic datasets.

Main Methods:

  • Developed BioinAI, integrating an intelligent system with DeepAdvancer (class-aware adversarial autoencoder) and stNiche (graph networks).
  • DeepAdvancer reconstructs gene expression profiles.
  • stNiche identifies functional cellular niches in spatial transcriptomics.
Keywords:
BioinformaticsComputational bioinformaticsTranscriptomics

Related Experiment Videos

Main Results:

  • Analysis of 49,738 samples across 1,016 datasets revealed a transcriptomic continuum and differential trajectory axes.
  • Identified shared immune responses and distinct fate determinants linking diverse diseases.
  • Discovered functional cellular niches, such as a fibroblast-immune niche in healthy skin, lost in pathological states.
  • BioinAI's conversational platform facilitates biological insight extraction.

Conclusions:

  • BioinAI provides a robust framework for integrating and analyzing large-scale omics data.
  • The framework enables the discovery of disease links through transcriptomic continuums and identification of critical cellular niches.
  • Facilitates deeper biological understanding and potential therapeutic targets from complex genomic datasets.