GLEAM: Galaxy Learning and Modeling

GLEAM (Galaxy Learning and Modeling) is a suite of machine learning tools for the Galaxy platform. Developed by the Goecks Lab, GLEAM empowers researchers to train models, generate predictions, and produce reproducible reports—all from a user-friendly interface without writing code.

Features

• Modern best practices for machine learning
• Reproducible and scalable workflows
• Machine learning support for diverse data types: tabular, image, text, categorical, and more
• Deep learning via Ludwig and automated ML via PyCaret
• Easy installation in Galaxy via XML wrappers
• Auto-generated visual reports

Available Tools

1. TabularLearner

Machine learning for structured tabular datasets using PyCaret.

• Train classification and regression models
• Evaluate performance and extract feature importance
• Generate predictions on new datasets
• Create interactive HTML reports

2. ImageLearner

Deep learning-based image classification using Ludwig.

• input files: Zip file with images and csv with metadata
• Tasks: classification
• Models available: ResNet, EfficientNet, VGG, Shufflenet, Vit, AlexNet and More...
• Output: Ludwig_model file, a report in the form of an HTML file (with learning curves, confusion matrices, and etc...), and a collection of CSV/json/png files containing the predictions, experiment stats and visualizations.

3. Multimodal Learner

AutoGluon-based training for datasets that mix tabular, text, and image columns.

• Ingests CSV/TSV labels with optional text fields and image paths (images supplied as ZIP archives)
• Supports classification and regression with quality presets, time limits, and deterministic mode
• Choose modern text and vision backbones while handling missing images and class balancing
• Produces metrics (JSON), training config (YAML), and an interactive HTML report for validation/test splits

4. Galaxy-Ludwig

General-purpose interface to Ludwig's full machine learning capabilities.

• Train and evaluate models on structured input (tabular, image, text, etc.)
• Expose Ludwig’s flexible configuration system
• Ideal for users needing advanced model customization

5. Galaxy-Digital Pathology Processing

Set of three specialized tools designed to transforms raw, large pathology images into a structured format, enabling the application of best practices for model development and ensuring data readiness for robust and efficient training.

• Image Tiler: Accepts .svs image format, which is the most common proprietary format for digital pathology whole slide images.
• Embedding Extractor: Leverages pre-trained models from the TorchVision foundation models for feature extraction (for example, ResNet50, EfficientNet_B0, DenseNet121).
• Multiple Instance Learning (MIL) Bag Processor: Facilitates the aggregation of embeddings from individual image tiles into "bags" using various pooling techniques (such as Max Pooling or Attention Pooling).

Installation

Install from Galaxy ToolShed (Recommended)

GLEAM tools are available in the Galaxy ToolShed and can be installed directly into your Galaxy instance:

1. Log in to your Galaxy instance as an administrator
2. Navigate to Admin → Install and Uninstall (or Manage Tools)
3. Search for the following tool suites under the goeckslab owner:
   • suite_tabular_learner - TabularLearner tools
   • suite_imagelearner - ImageLearner tools
   • suite_ludwig - Galaxy-Ludwig tools
   • suite_tiler - Image Tiler tool
   • suite_embedding_extractor - Embedding Extractor tool
   • suite_mil_bag - Multiple Instance Learning Bag Processor tool
4. Select the desired tool suites and click Install

Galaxy will automatically handle dependencies and configuration.

Manual Installation (Alternative)

If you prefer to install from source or need to modify the tools:

1. Clone the repository:

   git clone https://github.com/goeckslab/gleam.git

2. Add entries for each tool in your tool_conf.xml of your galaxy instance:

   <tool file="<path-to-your-local-tabularlearner/tabular_learner.xml>" />
   <tool file="<path-to-your-local-imagelearner/image_learner_train.xml>" />
   <tool file="<path-to-your-local-galaxy-ludwig/ludwig_train.xml>" />

Contributing

We welcome contributions. To propose new tools, report bugs, or suggest improvements:

1. Fork the repository

2. Create a feature branch

3. Commit and test your changes

4. Submit a pull request