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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jan 8, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.

Shixin Li1,2,3,4, Tianxiang Xiao1,2,3,4, Yuanyuan Lan1,2,3

  • 1State Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers advanced computational strategies for analyzing large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. This review explores AI

Keywords:
agentartificial intelligencedeep learningfoundation modelreviewsingle cell RNA‐seqspatial transcriptomics

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) generate complex, large-scale datasets.
  • Conventional computational methods face limitations in scalability and multimodal integration.
  • Artificial intelligence (AI) presents novel solutions for transcriptomic data analysis.

Purpose of the Study:

  • To review AI applications across the entire transcriptomic analysis workflow.
  • To trace the evolution and trends of AI models in transcriptomics.
  • To provide guidance for researchers and developers in AI tool selection and design.

Main Methods:

  • Survey of AI applications in transcriptomic data preprocessing.
  • Review of AI for downstream analyses: trajectory inference, gene regulatory network reconstruction, spatial domain detection.
  • Analysis of AI model advantages, limitations, and applicability.

Main Results:

  • AI enables advanced analysis and interpretation of complex transcriptomic data.
  • AI models show diverse trends and domain-specific applicability.
  • Key innovations, challenges, and future directions in AI for transcriptomics are identified.

Conclusions:

  • AI is a transformative force in transcriptomic data analysis.
  • This review offers practical guidance for AI model selection and development.
  • Future directions emphasize continued innovation in AI for biological discovery.