ArticleFrontiers in cellular neuroscience2024
PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.
Article in Frontiers in cellular neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting medical and omics data.bioRxiv : the preprint server for biology · 2026Article
- BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine.bioRxiv : the preprint server for biology · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.bioRxiv : the preprint server for biology · 2024Article
- mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.bioRxiv : the preprint server for biology · 2024Article
- AI for Scientific Discovery in Omics Data-Driven Precision Medicine.Missouri medicineArticle
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7 authors.
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Abstract
Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.
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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 Socratic graph.