AIMC Topic: Leukemia, Myeloid, Acute
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Consistent quantitative gene product expression: #2. Antigen intensities on bone marrow cells are invariant between individuals.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Oct 18, 2016
Five reference populations in bone marrow specimens were identified by flow cytometry using specific combinations of reagents in order define the variation of gene product expression intensities both within and between individuals. Mature lymphocytes...
Consistent quantitative gene product expression: #1. Automated identification of regenerating bone marrow cell populations using support vector machines.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Jul 14, 2016
Identification and quantification of maturing hematopoietic cell populations in flow cytometry data sets is a complex and sometimes irreproducible step in data analysis. Supervised machine learning algorithms present promise to automatically classify...
Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Aug 3, 2015
PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplanta...
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Kaplan-Meier Estimate
Time Factors
ROC Curve
Logistic Models
Treatment Outcome
Middle Aged
Decision Support Techniques
Registries
Risk Assessment
Humans
Data Mining
Retrospective Studies
Disease-Free Survival
Europe
Machine Learning
Postoperative Complications
Hematopoietic Stem Cell Transplantation
Female
Risk Factors
Algorithms
Disease Progression
Decision Trees
Transplantation, Homologous
Reproducibility of Results
Adult
Leukemia, Myeloid, Acute
Male
Predictive Value of Tests
Area Under Curve
Integrating GO and KEGG terms to characterize and predict acute myeloid leukemia-related genes.
Hematology (Amsterdam, Netherlands)
Oct 24, 2014
BACKGROUND/OBJECTIVE: Acute myeloid leukemia (AML) is a progressive and malignant cancer of myelogenous blood cells, which disturbs the production of normal blood cells. Although several risk and genetic factors (AML-related genes) have been investig...
A machine learning framework for cross-institute standardized analysis of flow cytometry in differentiating acute myeloid leukemia from non-neoplastic conditions.
Computers in biology and medicine
Jul 1, 2025
Flow cytometry (FC) remains a cornerstone diagnostic tool for acute myeloid leukemia (AML), yet standardizing panels across laboratories presents persistent challenges. Our study introduces a validated machine learning framework enabling cross-panel ...
Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes.
Blood
Jun 19, 2025
Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning mo...
Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.
PloS one
Jan 1, 2025
BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an in...
High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.
Journal of immunology (Baltimore, Md. : 1950)
Nov 15, 2024
Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse following hematopoietic stem cell transplant (HSCT) is critical for patient outcomes. We analyzed PBMCs from 58 patients with AML undergoing HSCT, focusing...
Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.
Medicine
Jul 12, 2024
Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patie...
Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.
The Lancet. Digital health
May 1, 2024
BACKGROUND: Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cells in the blood and bone marrow. Prompt and accurate diagnosis of the three main acute leukaemia subt...
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