AIMC Topic: Leukemia, Myeloid, Acute
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Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.
eLife
Nov 22, 2024
Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study...
Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques.
Leukemia research
Nov 20, 2024
BACKGROUND: Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), re...
Treatment Outcome
Follow-Up Studies
Graft vs Host Disease
Hematopoietic Stem Cell Transplantation
Adolescent
Child, Preschool
Child
Transplantation, Homologous
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Leukemia
Aged
Young Adult
Adult
Male
Middle Aged
Survival Rate
Leukemia, Myeloid, Acute
Prognosis
Retrospective Studies
Female
Humans
Machine Learning
Bloodstream Infections in Childhood Acute Myeloid Leukemia and Machine Learning Models: A Single-institutional Analysis.
Journal of pediatric hematology/oncology
Oct 22, 2024
Childhood acute myeloid leukemia (AML) requires intensive chemotherapy, which may result in life-threatening bloodstream infections (BSIs). This study evaluated whether machine learning (ML) could predict BSI using electronic medical records. All chi...
Considerations for using tree-based machine learning to assess causation between demographic and environmental risk factors and health outcomes.
Environmental science and pollution research international
Oct 12, 2024
Evaluation of the heterogeneous treatment effect (HTE) allows for the assessment of the causal effect of a therapy or intervention while considering heterogeneity in individual factors within a population. Machine learning (ML) methods have previousl...
Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls.
Biochimica et biophysica acta. Gene regulatory mechanisms
Oct 2, 2024
Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop ...
BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.
Journal of imaging informatics in medicine
Sep 25, 2024
Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-lin...
Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach.
Computer methods and programs in biomedicine
Sep 18, 2024
BACKGROUND AND OBJECTIVE: The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents ...
Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.
BMC cancer
Aug 12, 2024
Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute ...
Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.
Scientific reports
Aug 2, 2024
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and...
CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.
Nature communications
Jul 17, 2024
Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach a...
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