ArticleCancer informatics2019
Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression.
Article in Cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Random Survival Forest Versus Elastic-Net Regularized Cox Regression for Survival Prediction in Acute Myeloid Leukemia at Distinct Treatment Time Points: Model Performance Comparison Study.JMIR bioinformatics and biotechnology · 2026Article
- Pediatric leukemia: origins, pathogenesis, the role of microenvironment and immunological modulation.Frontiers in immunology · 2026Review
- Development of an interpretable machine learning model to predict complete remission and first adverse event in pediatric acute myeloid leukemia using routine clinical data.Frontiers in oncology · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Biomarkers and Mental Disorders: A Relevance Analysis Using a Random Forest Algorithm.Biomolecules · 2025Article
- Age-stratified machine learning identifies divergent prognostic significance of molecular alterations in AML.HemaSphere · 2025Article
- An efficient leukemia prediction method using machine learning and deep learning with selected features.PloS one · 2025Article
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
- An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia.Journal of clinical medicine · 2024Article
- A decision support system to recommend appropriate therapy protocol for AML patients.Frontiers in artificial intelligence · 2024Article
- A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters.Journal of cellular and molecular medicine · 2023Article
- Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.Haematologica · 2023Article
- Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures andBioinformatics advances · 2023Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.Scientific reports · 2022Article
- Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.Computational and structural biotechnology journal · 2022Article
- A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial.Cancers · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of treatment and the samples taken from patients who did not benefit. We tuned classifier hyperparameters to optimize performance and used multiple methods to guide our feature selection as well as our assessment of algorithm performance. To identify the model which performed best within the context of this study, we plotted receiver operating characteristic (ROC) curves. Using the top 75 genes from the
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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.