ArticleFrontiers in genetics2019
A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study.Respiratory research · 2025Article
- Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.Genome medicine · 2024Article
- Predicting drug response from single-cell expression profiles of tumours.BMC medicine · 2023Article
- Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.NAR genomics and bioinformatics · 2023Article
- A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response.Nature communications · 2022Article
- A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies.Frontiers in genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression in individual cells can now be measured for thousands of cells in a single experiment thanks to innovative sample-preparation and sequencing technologies. State-of-the-art computational pipelines for single-cell RNA-sequencing data, however, still employ computational methods that were developed for traditional bulk RNA-sequencing data, thus not accounting for the peculiarities of single-cell data, such as sparseness and zero-inflated counts. Here, we present a ready-to-use pipeline named
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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.