AIMC Topic: Leukemia, Myeloid, Acute

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Prediction of myeloid malignant cells in Fanconi anemia using machine learning.

PloS one
Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid maligna...

Integrated m6A reader network in acute myeloid leukemia: prognostic modeling, immune modulation, and functional validation of YTHDF3.

International immunopharmacology
Emerging evidence highlights RNA N6-methyladenosine (m6A) modifications as pivotal regulators of tumorigenesis, yet the synergistic roles of m6A readers in the pathogenesis and prognosis of acute myeloid leukemia (AML) remains unclear. By leveraging ...

Construction and validation of a risk prediction model for complications in patients with acute leukemia based on machine learning.

Scientific reports
Early-phase severe complications remain a major cause of morbidity and mortality during induction chemotherapy for acute leukaemia. Existing risk scores capture only limited prognostic variance and are rarely well-calibrated for clinical decision sup...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.

Scientific reports
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classificat...

Deciphering lactate/lactylation networks in AML: integrated scRNA-seq and transcriptomics reveal functions and prognostic model.

BMC cancer
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity, necessitating deep molecular characterization for precision therapy. Lactate metabolism and histone lactylation, influencing tumor biology via epigenetic regulation and immune microenvir...

Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.

JMIR medical informatics
BACKGROUND: Data scarcity and dispersion pose significant obstacles in biomedical research, particularly when addressing rare diseases. In such scenarios, synthetic data generation (SDG) has emerged as a promising path to mitigate the first issue. Co...

Acute myeloid leukemia classification using ReLViT and detection with YOLO enhanced by adversarial networks on bone marrow images.

Scientific reports
Acute myeloid leukemia (AML) is recognized as a highly aggressive cancer that affects the bone marrow and blood, making it the most lethal type of leukemia. The detection of AML through medical imaging is challenging due to the complex structural and...

Residual disease in NPM1-mutated acute myeloid leukemia.

Clinica chimica acta; international journal of clinical chemistry
Acute myeloid leukemia (AML) represents a genetically heterogeneous malignancy, with mutations in the nucleophosmin-1 (NPM1) gene identified as the most prevalent and clinically significant molecular biomarkers. These mutations play a crucial pivotal...

Unveiling the role of coagulation-related genes in acute myeloid leukemia prognosis and immune microenvironment through machine learning.

European journal of medical research
BACKGROUND: Acute Myeloid Leukemia (AML) is a highly heterogeneous hematologic malignancy influenced by various factors affecting prognosis. Recently, the role of coagulation-related genes in tumor biology has garnered increasing attention. This stud...

Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia.

Nature communications
Despite the critical role of DNA methylation, clinical implementations harnessing its promise have not been described in acute myeloid leukemia. Utilizing DNA methylation from 3314 leukemia patient samples across 11 harmonized cohorts, we describe th...