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Leukemia, Myeloid, Acute

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Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML)...

Machine learning applications in the diagnosis of leukemia: Current trends and future directions.

International journal of laboratory hematology
Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing tr...

A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient's Prognosis using Comprehensive Clinical Parameters.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. However, the challenges of accurate and consistent forecasting lie in the high variability of the disease ...

A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Blood advances
Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harb...

A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.

PloS one
Disease causing gene identification is considered as an important step towards drug design and drug discovery. In disease gene identification and classification, the main aim is to identify disease genes while identifying non-disease genes are of les...

Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network.

Annals of clinical and laboratory science
Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempted to determine a set of critical proteins that were associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Lea...

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

EBioMedicine
BACKGROUND: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from dr...

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Haematologica
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856...