ArticleNature communications2018
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 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
130 citing papers in PubMed, 1 synthesis or guideline pooled it, 339 citations in OpenAlex.
- Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2025Pooled it
- High-Throughput Drug Screening and Multi-Omic Analysis to Guide Individualized Treatment for Multiple Myeloma.JCO precision oncology · 2021Trial
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- The roles of chromatin remodeling and 3D genome organization in cancers: from mechanistic insights to emerging treatment options.Molecular cancer · 2026Review
- ELOVL1 promotes the progression of intrahepatic cholangiocarcinoma by enhancing endoplasmic reticulum stress and the PI3K/AKT/mTOR signaling pathway.Biology direct · 2026Article
- Article
- AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.Biomedical engineering and computational biology · 2026Article
- Pediatric leukemia: origins, pathogenesis, the role of microenvironment and immunological modulation.Frontiers in immunology · 2026Review
- Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data.Journal of medical systems · 2025Article
- Review
- High mtDNA content identifies oxidative phosphorylation-driven acute myeloid leukemias and represents a therapeutic vulnerability.Signal transduction and targeted therapy · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Advances and challenges in drug design against dental caries: Application ofJournal of pharmaceutical analysis · 2025Review
- Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments.NPJ precision oncology · 2025Article
- Every cloud has a silver lining: DeepSeek's light through acute respiratory distress syndrome shadows.Journal of thoracic disease · 2025Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- CD147 at the crossroads of glycoprotein networks, metabolic reprogramming, and metastatic progression.Frontiers in immunology · 2025Review
- The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review.Brain & spine · 2025Review
- Emerging functions of FMNL1 in myeloid neoplasms: insights from bioinformatics to biological and pharmacological landscapes.Translational cancer research · 2024Article
- Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.Briefings in bioinformatics · 2024Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.
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.