ArticleBiomolecules2023
A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- Airway cell-cell communication in smoking through integration of spatial and single-cell transcriptomics.Respiratory research · 2026Article
- Advances in sex-specific single-cell transcriptomic profiling in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.Reproduction (Cambridge, England) · 2026Review
- Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.Genome biology · 2026Article
- Intercellular signaling reinforces single-cell level phenotypic transitions and facilitates robust re-equilibrium of heterogeneous cancer cell populations.Cell communication and signaling : CCS · 2025Article
- Advances and challenges in cell-cell communication inference: a comprehensive review of tools, resources, and future directions.Briefings in bioinformatics · 2025Review
- Airway Spatial Transcriptomics in Smoking.medRxiv : the preprint server for health sciences · 2025Article
- Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics.PloS one · 2024Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.
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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.