AutoML for Breast Cancer Variant Pathogenicity Prediction: Optimizing Dataset Design for Genomic AI
Healthcare · Year: 2025
Computational and Structural Biotechnology Journal
Healthcare Research & Reproducible Machine Learning
Peer-reviewed healthcare machine learning research and applied medical data studies powered by structured experimentation, automated model benchmarking, and reproducible AutoML pipelines with MLJAR.
Recent Publications & Applied Machine Learning Case Studies
Explore peer-reviewed and applied machine learning studies built on structured experimentation and reproducible pipelines with MLJAR.
Deep Learning and AutoML for Fall Risk Prediction Using Vision-Based Gait Analysis in INPH
Healthcare · Year: 2025
Frontiers in Aging Neuroscience
Automated Machine Learning for Prostate Cancer Detection and Gleason Score Prediction Using MRI
Healthcare · Year: 2025
BMC Cancer
Predicting Recurrent SARS-CoV-2 Mutations Using Machine Learning
Healthcare · Year: 2022
International Journal of Molecular Sciences
AutoML and Ensemble Machine Learning for Right Heart Failure Prediction in Primary Pulmonary Hypertension
Healthcare · Year: 2022
Heart (British Cardiovascular Society Conference)
AutoML and Ensemble Learning for Post-Heart Failure Survival Prediction Using MLJAR
Healthcare · Year: 2021
American Heart Journal
AutoML for NAFLD Screening Diagnosis Using MLJAR: Zero-Code Machine Learning Models Based on Anthropometric Measures
Healthcare · Year: 2020
medRxiv
Why Researchers and ML Engineers Choose MLJAR Studio
A private, AI-powered Python notebook designed for reproducible machine learning experiments, structured benchmarking, and applied research workflows.
Reproducible Machine Learning Experiments
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Local-First Execution & Data Control
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Autonomous Model Benchmarking & Optimization
Automatically compare candidate models, perform cross-validation, and run hyperparameter optimization with full metric visibility.
Research Domains
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