Integrated Gene Expression Resource for Multi-Subtype Acute Myeloid Leukemia: A Global Reference Dataset

Acute Myeloid Leukemia (AML) is a highly heterogeneous hematologic malignancy with multiple clinically relevant subtypes. Despite the availability of numerous gene expression datasets, there is no comprehensive, balanced, and multi-cohort resource integrating all major AML subtypes. Here, we present an integrated gene expression resource for seven key AML subtypes, curated from eight publicly available GEO datasets (GSE13159, GSE6891, GSE14468, GSE15434, GSE10358, GSE61804, GSE71014, GSE12417). The dataset comprises 338 samples with standardized metadata, consistent labeling, and batch-corrected expression matrices. Advanced preprocessing, label harmonization, and balanced sampling ensure equal representation across subtypes. This resource is ready for machine learning, biomarker discovery, and cross-study validation, providing a global reference dataset for AML research.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-30
DOI
https://doi.org/10.5281/zenodo.17008407
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Integrated Gene Expression Resource for Multi-Subtype Acute Myeloid Leukemia: A Global Reference Dataset

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Integrated Gene Expression Resource for Multi-Subtype Acute Myeloid Leukemia: A Global Reference Dataset

article en

Abstract

Acute Myeloid Leukemia (AML) is a highly heterogeneous hematologic malignancy with multiple clinically relevant subtypes. Despite the availability of numerous gene expression datasets, there is no comprehensive, balanced, and multi-cohort resource integrating all major AML subtypes. Here, we present an integrated gene expression resource for seven key AML subtypes, curated from eight publicly available GEO datasets (GSE13159, GSE6891, GSE14468, GSE15434, GSE10358, GSE61804, GSE71014, GSE12417). The dataset comprises 338 samples with standardized metadata, consistent labeling, and batch-corrected expression matrices. Advanced preprocessing, label harmonization, and balanced sampling ensure equal representation across subtypes. This resource is ready for machine learning, biomarker discovery, and cross-study validation, providing a global reference dataset for AML research.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 58%
Geochemistry and Geologic Mapping
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