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

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

29 Mar 2021

Abstract excerpt

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using...

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 Cancer Genomics and Diagnostics
Share to Myeloproliferative Neoplasms: Diagnosis and Treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 33854980
DOI: 10.3389/fonc.2021.657191

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression ProfilingPMID 33854980Read ↗

Survival prediction in acute myeloid leukemia using gene expression profiling.Semantic proximity Read ↗

Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome predictionSemantic proximity Read ↗

Development and Validation of a Novel RNA Sequencing–Based Prognostic Score for Acute Myeloid LeukemiaSemantic 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 ↗

A Novel 85-Gene Expression Signature Predicts Unfavorable Prognosis in Acute Myeloid Leukemia.Semantic proximity Read ↗

Predictive Power of Machine Learning Models for Relapse Outcomes in Acute Myeloid Leukemia: Unveiling Key Genes and Pathways for Improved Patient ManagementSemantic proximity Read ↗

A novel 4‐mRNA signature predicts the overall survival in acute myeloid leukemiaSemantic proximity Read ↗

A Prognostic Model of Seven Immune Genes to Predict Overall Survival in Childhood Acute Myeloid LeukemiaSemantic proximity Read ↗

AML Risk Stratification Models Utilizing ELN-2017 Guidelines and Additional Prognostic Factors: A SWOG ReportSemantic proximity Read ↗

Genomic analysis of biomarkers related to the prognosis of acute myeloid leukemiaSemantic proximity Read ↗

Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.Semantic proximity Read ↗

Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid LeukemiaSemantic proximity Read ↗

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