Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.

doi:10.1182/blood.2020010603

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Community

Back to search

Article

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.

Blood - 11 Nov 2021

Awada Hassan, Durmaz Arda, Gurnari Carmelo, Kishtagari Ashwin, Meggendorfer Manja, Kerr Cassandra M, Kuzmanovic Teodora, Durrani Jibran, Shreve Jacob, Nagata Yasunobu, Radivoyevitch Tomas, Advani Anjali S, Ravandi Farhad, Carraway Hetty E, Nazha Aziz, Haferlach Claudia, Saunthararajah Yogen, Scott Jacob, Visconte Valeria, Kantarjian Hagop, Kadia Tapan, Sekeres Mikkael A, Haferlach Torsten, Maciejewski Jaroslaw P

Abstract excerpt

Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bayes Theorem

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cytogenetics

Share to Gene Expression Regulation, Leukemic

Share to Genomics

Share to Leukemia, Myeloid, Acute

Share to Machine Learning

Share to Neoplasms, Second Primary

Share to Translocation, Genetic

Source

Open in PubMed Open full text

Identifiers

PMID: 34075412
DOI: 10.1182/blood.2020010603
PMCID: PMC8767789

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.PMID 34075412Read ↗

Unified classification and risk-stratification in Acute Myeloid LeukemiaSemantic proximity Read ↗

Molecular patterns identify distinct subclasses of myeloid neoplasia.Semantic proximity Read ↗

Genomics of Acute Myeloid Leukemia Diagnosis and Pathways.Semantic proximity Read ↗

Genomic Classification and Prognosis in Acute Myeloid Leukemia.Semantic proximity Read ↗

Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignanciesSemantic proximity Read ↗

Molecular patterns identify distinct subclasses of myeloid neoplasiaSemantic proximity Read ↗

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insightsSemantic proximity Read ↗

Classification and risk assessment in AML: integrating cytogenetics and molecular profilingSemantic proximity Read ↗

The genomic landscape of acute myeloid leukemia: Redefining classifications, ontogeny, and therapeutic strategies.Semantic proximity Read ↗

Diagnosis and Therapy of Acute Myeloid Leukemia in the Era of Molecular Risk Stratification.Semantic proximity Read ↗

A new genomic framework to categorize pediatric acute myeloid leukemia.Semantic proximity Read ↗

Biomarkers in Acute Myeloid Leukemia: Leveraging Next Generation Sequencing Data for Optimal Therapeutic StrategiesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.