JADBio vs LightAutoML

JADBio is a no-code automated machine learning platform for life scientists analyzing public or study data. It accepts curated CSV and other delimited files, and users choose the outcome to predict. Supported data includes genomics, transcriptome, metagenome, proteome, metabolome, clinical data, and images. Analysis features include automated preprocessing, feature selection, survival analysis, predictive modeling, and model interpretation; outputs can include predictive models, biomarkers or biosignatures, visualizations, and information for applying models. The REST API can add AutoML, including image analysis, to applications and automate workflows, with a Python client available through GitHub, PyPI, and Anaconda. JADBio offers web, API, and self-hosted options. Basic is free and includes one seat, three projects, a 50 MB upload limit, 500 MB storage, and one model export. Team is listed at $2,199 a year, billed annually, and includes a 14-day free trial. Business Pro has custom pricing and lists SaaS, AWS container, and on-premise delivery.

LightAutoML is an open-source Python library for automated machine learning on tabular and text data. It supports binary and multiclass classification and regression, using ready-made tabular, text and WhiteBox presets or modular custom pipelines. Pipeline creation can automate data processing and typing, feature selection, hyperparameter tuning, time utilization and report creation. Documented model classes include linear models, LightGBM and CatBoost boosted trees, neural networks and a WhiteBox scorecard. Its basic tutorial says the library can handle missing values and outliers automatically. Optional installation extras cover NLP, computer vision and reports, and a tutorial demonstrates a SQL data source. Installation is through PyPI with `pip install lightautoml`; the project is licensed under Apache License, Version 2.0. The maker describes it as a way for data scientists and analysts to reduce routine data preparation and model selection work. The current package handles datasets with independent samples in each row; multitable datasets and sequences remain in progress.