ArticleF1000Research2019
Using singscore to predict mutation status in acute myeloid leukemia from transcriptomic signatures.
Article in F1000Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 27 citations in OpenAlex.
- A computational framework integrating multi-omics and machine learning for identifying glycolytic gene markers in breast cancer.Discover oncology · 2026Article
- Integrating single-cell RNA-seq and machine learning to dissect polyamine metabolism in metabolic dysfunction-associated steatotic liver disease.Frontiers in medicine · 2026Article
- A cell-state axis underlying colonization in carcinomas with implications for metastasis risk prediction and interception.Cell reports · 2025Article
- IGFBP7 and CCT2 are novel lactylation-driven mediators of endothelial-to-mesenchymal transition in idiopathic pulmonary fibrosis.Functional & integrative genomics · 2025Article
- Acupuncture regulates astrocyte neurotoxic polarization to protect blood-brain barrier integrity in delayed thrombolysis through mediating ERK1/2/Cx43 axis.IBRO neuroscience reports · 2025Article
- The Molecular Characteristics and Therapeutic Implications of O-glycan Synthesis in Pancreatic Cancer by Integrating Transcriptome and Single-cell Data.Current medicinal chemistry · 2025Article
- Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.Frontiers in immunology · 2025Article
- Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in cancer cells by machine learning.Frontiers in immunology · 2025Article
- A multi-organ map of the human immune system across age, sex and ethnicity.bioRxiv : the preprint server for biology · 2024Article
- Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Machine learning approach informs biology of cancer drug response.BMC bioinformatics · 2022Article
- Article
- The Architecture of a Precision Oncology Platform.Advances in experimental medicine and biology · 2022Review
- SLUG-related partial epithelial-to-mesenchymal transition is a transcriptomic prognosticator of head and neck cancer survival.Molecular oncology · 2022Article
- Article
- Gene expression signatures as candidate biomarkers of response to PD-1 blockade in non-small cell lung cancers.PloS one · 2021Article
- Stable gene expression for normalisation and single-sample scoring.Nucleic acids research · 2020Article
- Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.Genome biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in RNA sequencing (RNA-seq) technologies that measure the transcriptome of biological samples have revolutionised our ability to understand transcriptional regulatory programs that underpin diseases such as cancer. We recently published singscore - a single sample, rank-based gene set scoring method which quantifies how concordant the transcriptional profile of individual samples are relative to specific gene sets of interest. Here we demonstrate the application of singscore to investigate transcriptional profiles associated with specific mutations or genetic lesions in acute myeloid leukemia. Using matched genomic and transcriptomic data available through the TCGA we show that scoring of appropriate signatures can distinguish samples with corresponding mutations, reflecting the ability of these mutations to drive aberrant transcriptional programs involved in leukemogenesis. We believe the singscore method is particularly useful for studying heterogeneity within a specific subsets of cancers, and as demonstrated, we show the ability of singscore to identify where alternative mutations appear to drive similar transcriptional programs.
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.