Trial reportBlood advances2019
A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.
Trial report in Blood advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Genetic deletion of JAM-C in preleukemic cells rewires leukemic stem cell gene expression program in AML.Blood advances · 2024Trial
- Trial
- Single cell multi-omics enables high-resolution identification and functional purification of human acute myeloid leukemia stem cells.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- The Clinical Relevance and Prognostic Significance of Calcitonin Receptor-Like (CALCRL) Gene Expression in AML Patients.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- A Five-Gene Signature for the Prediction of Event-Free Survival of Both Pediatric and Adult Acute Myeloid Leukemia.Diagnostics (Basel, Switzerland) · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Flow cytometric detection of leukemic stem cells in Acute Myeloid Leukemia: current status and future directions.Frontiers in pharmacology · 2025Review
- Deciphering cell states and the cellular ecosystem to improve risk stratification in acute myeloid leukemia.Briefings in bioinformatics · 2024Article
- Article
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- [Expression characteristics and clinical significance of CD109 in de novo acute myeloid leukemia].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2023Article
- TowardsGenes · 2023Article
- Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia.Translational cancer research · 2023Article
- Mitochondrial gene expression signature predicts prognosis of pediatric acute myeloid leukemia patients.Frontiers in oncology · 2023Article
- TP53-Mutated Myelodysplasia and Acute Myeloid Leukemia.Mediterranean journal of hematology and infectious diseases · 2023Review
- Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia.The Journal of clinical investigation · 2022Article
- A 13-gene signature to predict the prognosis and immunotherapy responses of lung squamous cell carcinoma.Scientific reports · 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
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harbor normal or risk-indeterminate karyotypes. Therefore, identifying more effective biomarkers predictive of treatment success and failure is essential for informing tailored therapeutic decisions. We applied an artificial neural network (ANN)-based machine learning approach to a publicly available data set for a discovery cohort of 593 adults with nonpromyelocytic AML. ANN analysis identified a parsimonious 3-gene expression signature comprising
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.