ReviewComputational and structural biotechnology journal2022
Emerging artificial intelligence applications in Spatial Transcriptomics analysis.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.Journal for immunotherapy of cancer · 2025Trial
- Multi-omics profiling of pancreatic neuroendocrine tumors: Interplay of genomic instability and the tumor microenvironment (Review).Oncology letters · 2026Review
- Brain Spatial Genomics Atlases.Genes · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.NPJ precision oncology · 2026Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Opportunities for RNA sequencing in physiology: from big data to understanding homeostasis and heterogeneity.Function (Oxford, England) · 2026Review
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Next generation sequencing and beyond: a review of genomic sequencing methods.Functional & integrative genomics · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Spatial transcriptomics: a bibliometric analysis with large language model on English literatures.Briefings in bioinformatics · 2025Article
- Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.Genome research · 2025Article
- Combining spatial transcriptomics with tissue morphology.Nature communications · 2025Review
- Digital spatial profiling for pathologists.Virchows Archiv : an international journal of pathology · 2025Article
- Benchmarking the translational potential of spatial gene expression prediction from histology.Nature communications · 2025Article
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.