ArticleGenes2021
A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 10 citations in OpenAlex.
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- Genetic Burden in Congenital Anomalies of the Mitral and Tricuspid Valves: A Case-Control Study.Pediatric cardiology · 2026Article
- Single-Cell Analysis Reveals Fibroblast-Derived Migrasomes as CXCL12 Carriers Promoting Skin Wound Repair.Journal of extracellular vesicles · 2025Article
- A natural language processing system for the efficient extraction of cell markers.Scientific reports · 2024Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- On the Origins of Omicron's Unique Spike Gene Insertion.Vaccines · 2022Article
- Genetic alteration of human MYH6 is mimicked by SARS-CoV-2 polyprotein: mapping viral variants of cardiac interest.Cell death discovery · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney
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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.