Leukemia

How artificial intelligence is transforming leukemia detection, diagnosis, and treatment.

109 Papers Open Access

Showing all 109 papers

Cited by 1236
Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity.

Cell, 2019

Open Access
Cited by 1092
Swarm Learning for decentralized and confidential clinical machine learning.

Nature, 2021

Open Access
Cited by 449
Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks.

Technol Cancer Res Treat, 2018

Open Access
Cited by 434
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Nat Commun, 2018

Open Access
Cited by 328
Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features.

Sci Rep, 2020

Open Access
Cited by 278
Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network.

Diagnostics (Basel), 2019

Open Access
Cited by 222
Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance.

Cancers (Basel), 2021

Open Access
Cited by 208
IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.

J Healthc Eng, 2020

Open Access
Cited by 204
Sensitive detection of rare disease-associated cell subsets via representation learning.

Nat Commun, 2017

Open Access
Cited by 189
Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes.

J Clin Oncol, 2021

Open Access
Cited by 178
Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks.

Nat Commun, 2016

Open Access
Cited by 174
Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution.

Cell, 2016

Open Access
Cited by 170
Identification of fusion genes and characterization of transcriptome features in T-cell acute lymphoblastic leukemia.

Proc Natl Acad Sci U S A, 2018

Open Access
Cited by 167
Single-cell profiling reveals the trajectories of natural killer cell differentiation in bone marrow and a stress signature induced by acute myeloid leukemia.

Cell Mol Immunol, 2021

Open Access
Cited by 163
Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse.

Nat Med, 2018

Open Access
Cited by 153
Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia.

Sci Rep, 2017

Open Access
Cited by 147
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Nat Commun, 2021

Open Access
Cited by 140
Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

Leukemia, 2022

Open Access
Cited by 139
Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Nat Commun, 2020

Open Access
Cited by 139
Multiparameter prediction of myeloid neoplasia risk.

Nat Genet, 2023

Open Access
Cited by 137
Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening.

Nat Biotechnol, 2023

Open Access
Cited by 137
Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Lab Invest, 2020

Open Access
Cited by 136
A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Haematologica, 2018

Open Access
Cited by 134
Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images.

Healthcare (Basel), 2022

Open Access
Cited by 131
Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology.

JCO Clin Cancer Inform, 2023

Open Access
Cited by 124
SETBP1 induces transcription of a network of development genes by acting as an epigenetic hub.

Nat Commun, 2018

Open Access
Cited by 121
Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods

Comput Intell Neurosci, 2021

Open Access
Cited by 120
Prediction of Outcome in Patients With Chronic Lymphocytic Leukemia Treated With Ibrutinib: Development and Validation of a Four-Factor Prognostic Model.

J Clin Oncol, 2021

Open Access
Cited by 117
The power and potential of integrated diagnostics in acute myeloid leukaemia.

Br J Haematol, 2020

Open Access
Cited by 116
Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL.

Nat Commun, 2020

Open Access
Cited by 112
Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques.

Sensors (Basel), 2022

Open Access
Cited by 111
Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.

EBioMedicine, 2018

Open Access
Cited by 106
The mutational oncoprint of recurrent cytogenetic abnormalities in adult patients with de novo acute myeloid leukemia.

Leukemia, 2017

Open Access
Cited by 103
Universal monitoring of minimal residual disease in acute myeloid leukemia.

JCI Insight, 2018

Open Access
Cited by 102
Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.

Blood Adv, 2020

Open Access
Cited by 98
A balanced iterative random forest for gene selection from microarray data.

BMC Bioinformatics, 2013

Open Access
Cited by 93
Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.

iScience, 2020

Open Access
Cited by 90
Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears.

BMC Cancer, 2022

Open Access
Cited by 87
SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

BMC Genomics, 2016

Open Access
Cited by 83
Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology.

Biomed Opt Express, 2017

Open Access
Cited by 82
CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.

Nat Commun, 2024

Open Access
Cited by 81
An Attention-Based Deep Learning for Acute Lymphoblastic Leukemia Classification

Sci Rep, 2024

Open Access
Cited by 74
Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care.

Oncologist, 2019

Open Access
Cited by 74
Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level.

PLoS One, 2019

Open Access
Cited by 74
Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Chem Commun (Camb), 2019

Open Access
Cited by 73
Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence.

NPJ Digit Med, 2024

Open Access
Cited by 72
Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Diagnostics (Basel), 2023

Open Access
Cited by 71
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Genome Biol, 2021

Open Access
Cited by 71
Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy.

Cell Rep, 2021

Open Access
Cited by 70
AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research.

Medicine (Baltimore), 2020

Open Access
Cited by 69
(18)F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia.

Theranostics, 2019

Open Access
Cited by 68
Label-Free Leukemia Monitoring by Computer Vision.

Cytometry A, 2020

Open Access
Cited by 68
Pharmacogenomic and Pharmacotranscriptomic Profiling of Childhood Acute Lymphoblastic Leukemia: Paving the Way to Personalized Treatment.

Genes (Basel), 2019

Open Access
Cited by 65
A Hematologist-Level Deep Learning Algorithm (BMSNet) for Assessing the Morphologies of Single Nuclear Balls in Bone Marrow Smears: Algorithm Development.

JMIR Med Inform, 2020

Open Access
Cited by 65
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Nat Biomed Eng, 2023

Open Access
Cited by 63
3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia.

Nat Commun, 2021

Open Access
Cited by 63
A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Blood Adv, 2019

Open Access
Cited by 63
The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Hemasphere, 2023

Open Access
Cited by 60
Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression.

Cancer Inform, 2019

Open Access
Cited by 59
Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.

Nat Commun, 2024

Open Access
Cited by 58
Circular RNA regulatory network reveals cell-cell crosstalk in acute myeloid leukemia extramedullary infiltration.

J Transl Med, 2018

Open Access
Cited by 55
Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study.

PLoS One, 2016

Open Access
Cited by 54
Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images

Front Oncol, 2023

Open Access
Cited by 54
Multiplexed single-cell morphometry for hematopathology diagnostics.

Nat Med, 2020

Open Access
Cited by 53
Acute myeloid leukemia and artificial intelligence

Best Pract Res Clin Haematol, 2020

Open Access
Cited by 53
Explainable Artificial Intelligence for Precision Medicine in Acute Myeloid Leukemia

Front Immunol, 2022

Open Access
Cited by 53
Hypomethylation coordinates antagonistically with hypermethylation in cancer development: a case study of leukemia.

Hum Genomics, 2016

Open Access
Cited by 51
A Machine Learning and Deep Learning-Based Integrated Multi-Omics Technique for Leukemia Prediction

Heliyon, 2024

Open Access
Cited by 51
DNA methylation markers in the diagnosis and prognosis of common leukemias.

Signal Transduct Target Ther, 2020

Open Access
Cited by 51
Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.

Haematologica, 2023

Open Access
Cited by 50
Molecular patterns identify distinct subclasses of myeloid neoplasia.

Nat Commun, 2023

Open Access
Cited by 49
Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients.

Leukemia, 2023

Open Access
Cited by 49
Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

Sci Adv, 2021

Open Access
Cited by 48
Characterization of a set of tumor suppressor microRNAs in T cell acute lymphoblastic leukemia.

Sci Signal, 2014

Open Access
Cited by 47
Deep Learning for Acute Myeloid Leukemia Diagnosis

J Med Life, 2020

Open Access
Cited by 46
Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression.

Blood Adv, 2022

Open Access
Cited by 45
Integrative analysis reveals functional and regulatory roles of H3K79me2 in mediating alternative splicing.

Genome Med, 2018

Open Access
Cited by 45
Prognostic and biologic significance of long non-coding RNA profiling in younger adults with cytogenetically normal acute myeloid leukemia.

Haematologica, 2017

Open Access
Cited by 44
Advancing leukemia diagnostics: Role of Next Generation Sequencing (NGS) in acute myeloid leukemia.

Hematol Rep, 2020

Open Access
Cited by 43
Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia.

Sci Rep, 2020

Open Access
Cited by 42
Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes.

Nat Commun, 2022

Open Access
Cited by 42
Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree.

Blood Cancer Discov, 2022

Open Access
Cited by 41
Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures.

Nat Commun, 2021

Open Access
Cited by 40
Network-guided modeling allows tumor-type independent prediction of sensitivity to all-trans-retinoic acid.

Ann Oncol, 2017

Open Access
Cited by 39
RNA editing signature during myeloid leukemia cell differentiation.

Leukemia, 2017

Open Access
Cited by 38
Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube

Sci Rep, 2024

Open Access
Cited by 37
Cut-like homeobox 1 (CUX1) tumor suppressor gene haploinsufficiency induces apoptosis evasion to sustain myeloid leukemia.

Nat Commun, 2021

Open Access
Cited by 37
Identifying Patients with Polycythemia Vera at Risk of Thrombosis after Hydroxyurea Initiation: The Polycythemia Vera-Advanced Integrated Models (PV-AIM) Project.

Biomedicines, 2023

Open Access
Cited by 36
Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia.

Sci Rep, 2020

Open Access
Cited by 36
Urolithin A and B Alter Cellular Metabolism and Induce Metabolites Associated with Apoptosis in Leukemic Cells.

Int J Mol Sci, 2021

Open Access
Cited by 35
Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry

bioRxiv, 2023

Open Access
Cited by 34
Blockade of deubiquitinase YOD1 degrades oncogenic PML/RARĪ± and eradicates acute promyelocytic leukemia cells.

Acta Pharm Sin B, 2022

Open Access
Cited by 32
Meta-analysis of gene expression in relapsed childhood B-acute lymphoblastic leukemia.

BMC Cancer, 2017

Open Access
Cited by 30
Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Cancers (Basel), 2024

Open Access
Cited by 28
Deep Learning Model for Differentiating Acute Myeloid and Lymphoblastic Leukemia in Peripheral Blood Cell Images

Digit Health, 2024

Open Access
Cited by 28
Leukemia Detection and Classification Using Computer-Aided Diagnosis System with Falcon Optimization Algorithm and Deep Learning

Sci Rep, 2024

Open Access
Cited by 27
A three-gene expression-based risk score can refine the European LeukemiaNet AML classification.

J Hematol Oncol, 2016

Open Access
Cited by 22
Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

Bioengineering (Basel), 2024

Open Access
Cited by 19
Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning

Cancer Sci, 2025

Open Access
Cited by 18
Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Comput Intell Neurosci, 2022

Open Access
Cited by 18
Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence

Diagnostics (Basel), 2022

Open Access
Cited by 15
ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia

PeerJ Comput Sci, 2025

Open Access
Cited by 15
Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis

Front Big Data, 2024

Open Access
Cited by 14
Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework

Sensors (Basel), 2024

Open Access
Cited by 12
An Efficient Leukemia Prediction Method Using Machine Learning and Deep Learning with Selected Features

PLoS One, 2025

Open Access
Cited by 7
An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization

Sci Rep, 2025

Open Access
Cited by 6
Artificial Intelligence Powered Automated and Early Diagnosis of Acute Lymphoblastic Leukemia Cancer in Histopathological Images

Int J Telemed Appl, 2025

Open Access
Cited by 3
A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review

Cureus, 2024

Open Access
Cited by 3
AI-Assisted Detection for Early Screening of Acute Myeloid Leukemia Using Infrared Spectra and Clinical Biochemical Reports of Blood

Bioengineering (Basel), 2025

Open Access