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Enterprise Layer Intelligence
Categories/Healthcare & Life Sciences/JADBio AutoML
JADBio AutoML

JADBio AutoML

Founded 2013

Discover biomarkers and build predictive healthcare models from multi-omics data without writing code

Pricing model

Free Tier, Paid

You can use JADBio to analyze complex biological data and build predictive machine learning models without writing code. The system accepts genomics, transcriptomics, proteomics, metabolomics, and clinical datasets to discover diagnostic biomarkers and molecular biosignatures. Researchers can automatically select relevant features, predict patient survival times, identify disease subtypes, and evaluate treatment responses. It eliminates manual algorithm tuning by testing multiple statistical methods and producing clear, interpretable results that medical researchers and data teams can immediately apply to drug discovery and clinical trial development.

What JADBio AutoML does

Upload multi-omics datasets for automated feature selectionTrain classification and regression models without writing codeExport biomarker signatures for clinical assay developmentPerform time-to-event survival analysis on patient cohortsValidate predictive model performance against external dataInterpret molecular feature contributions to patient outcomesBuild machine learning models on multi-omics data without codingAutomates biomarker discovery and compact feature selectionParses genomics, transcriptomics, metabolomics, and proteomics datasetsCalculates time-to-event predictions and survival analysisGenerates compact biosignatures for disease classificationProduces interpretable models for clinical decision support

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
BasicFree
Team 5+ (per team) (annual billing)$2,199 / mo, billed annually

Frequently asked

AWS Marketplace, REST API, Python Client, Public Data Repositories, Amazon S3, SciKit-Learn (via API), Bioinformatics Pipelines, Excel/CSV Connectors

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JADBio, AutoML, biomarker discovery, multi-omics, genomics, proteomics, metabolomics, predictive modeling, machine learning, bioinformatics, precision medicine, feature selection, cancer research, healthcare data, clinical diagnostics