Discover Community Create You

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

Article

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Blood - 12 Nov 2020

Nagata Yasunobu, Zhao Ran, Awada Hassan, Kerr Cassandra M, Mirzaev Inom, Kongkiatkamon Sunisa, Nazha Aziz, Makishima Hideki, Radivoyevitch Tomas, Scott Jacob G, Sekeres Mikkael A, Hobbs Brian P, Maciejewski Jaroslaw P

Abstract excerpt

Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS using a...

Read the complete abstract on PubMed

Topics

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

Share to Acute Myeloid Leukemia Research
Share to Aged

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cancer Genomics and Diagnostics

Share to Genetic Association Studies

Share to Machine Learning

Share to Mutation

Share to Myelodysplastic Syndromes

Share to Myeloproliferative Neoplasms: Diagnosis and Treatment

Source

Open in PubMed Open full text

Identifiers

PMID: 32961553
DOI: 10.1182/blood.2020005488
PMCID: PMC7702479

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 demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.PMID 32961553Read ↗

Molecular taxonomy of myelodysplastic syndromes and its clinical implications.Semantic proximity Read ↗

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

Molecular patterns identify distinct subclasses of myeloid neoplasiaSemantic proximity Read ↗

Prognostic mutation constellations in acute myeloid leukaemia and myelodysplastic syndrome.Semantic proximity Read ↗

Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS.Semantic proximity Read ↗

Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia.Semantic proximity Read ↗

Diagnosis and classification of myelodysplastic syndromesSemantic proximity Read ↗

Implications of molecular genetic diversity in myelodysplastic syndromes.Semantic proximity Read ↗

Landscape of genetic lesions in 944 patients with myelodysplastic syndromes.Semantic proximity Read ↗

The spectrum of genetic mutations in myelodysplastic syndrome: Should we update prognostication?Semantic proximity Read ↗

Clinical, Genetic, and Pathologic Variability in Myelodysplastic Syndromes and Precursor Conditions Across Race, Ethnicity, and Sex.Semantic proximity Read ↗

Molecular landscape of myelodysplastic neoplasms in disease classification and prognostication.Semantic proximity Read ↗

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