Statistics and Machine Learning — Editorial Board Home

This is a new, experimental "Editorial Board Home" for a given topic. It is intended to provide a single place for maintainers and editorial board members to find out key information about their topic and identify action items.

Editorial Board

orcid logoFabio Cumbo avatar Fabio Cumboorcid logoAnup Kumar avatar Anup KumarMarzia A Cremona avatar Marzia A Cremona

Action Items

Item Status Why you should do this
Summary Done ✅ Provide a sufficiently detailed summary of the topic to let learners know what they're learning about in this topic.
Sufficient Editorial Board Members Done ✅ (3 members) Having multiple people sharing the burden of being responsible for a specific topic can reduce board member burn-out in the long term.
Enable Subtopics Done ✅ Subtopics help organize the content and make it easier to navigate.
Annotate Funders Done ✅ (5 funders) By annotating the funders of your topic's materials, you make it easier to write your grant reports later
Learning Pathway CTA Pending ❌ By providing a Learning Pathway CTA, we can help guide learners to the best resources for learning about this topic.
Review Feedback Received Ongoing 🔁 Review all feedback received for the Statistics and Machine Learning topic and create GitHub issues or PRs where needed.
Review open Pull Requests Ongoing 🔁 View all open PRs in the Statistics and Machine Learning topic and review, comment and merge!
Review open GitHub Issues Ongoing 🔁 View all open issues related to the Statistics and Machine Learning topic and comment, fix, or ask others for help!

Topic Materials

Material Contributions v2 Pre-requisites Follow up trainings Data on Zenodo Notebook Server Compatibility
Foundational Aspects of Machine Learning using Python ✅ ✅ ✅ ✅
Pretraining a Large Language Model (LLM) from Scratch on DNA Sequences ✅ ✅ ✅ ✅
Fine-tuning a LLM for DNA Sequence Classification ✅ ✅ ✅ ✅
Predicting Mutation Impact with Zero-shot Learning using a pretrained DNA LLM ✅ ✅ ✅ ✅
Generating Artificial Yeast DNA Sequences using a DNA LLM ✅ ✅ ✅ ✅
Optimizing DNA Sequences for Biological Functions using a DNA LLM ✅ ✅ ✅ ✅
Identifing Survival Markers of Brain tumor with Flexynesis ❌ ✅
Modeling Breast Cancer Subtypes with Flexynesis ❌ ✅
Prepare Data from CbioPortal for Flexynesis Integration ❌ ✅
Unsupervised Analysis of Bone Marrow Cells with Flexynesis ❌ ✅
Neural networks using Python ✅ ✅ ✅ ✅
Deep Learning (without Generative Artificial Intelligence) using Python ✅ ✅ ✅ ✅
Regulations/standards for AI using DOME ✅ ✅
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy ✅ ✅ ✅
Age prediction using machine learning ✅ ✅
Basics of machine learning ✅ ✅
Classification in Machine Learning ✅ ✅
Clustering in Machine Learning ✅ ✅
Comparing ligand-binding site predictions across different protein structure modalities using SIMORGH ❌ ✅
Deep Learning (Part 1) - Feedforward neural networks (FNN) ❌ ✅ ✅ ✅
Deep Learning (Part 2) - Recurrent neural networks (RNN) ❌ ✅ ✅ ✅
Deep Learning (Part 3) - Convolutional neural networks (CNN) ❌ ✅ ✅
Fine tune large protein model (ProtTrans) using HuggingFace ✅ ✅ ✅
GLEAM Image Learner - Validating Skin Lesion Classification on HAM10000 ❌ ✅
GTEx Tissue Modeling with Galaxy Image Learner ❌ ✅
Galaxy Tabular Learner - Building a Model using Chowell clinical data ❌ ✅
Gleam Multimodal Learner - Head and Neck cancer Recurrence Prediction with HANCOCK ❌ ✅
Image classification in Galaxy with fruit 360 dataset ❌ ✅ ✅
Interval-Wise Testing for omics data ❌ ✅
Introduction to Machine Learning using R ✅ ✅ ✅ ❌
Introduction to deep learning ✅ ✅
Machine learning: classification and regression ✅ ✅
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis ❌ ✅
Regression in Machine Learning ✅ ✅
Supervised Learning with Hyperdimensional Computing ❌ ✅
Text-mining with the SimText toolset ❌ ✅
Train and Test a Deep learning image classifier with Galaxy-Ludwig ❌ ✅

Topic Workflows

Material Workflow Updated Version Tests Reports Comments
Identifing Survival Markers of Brain tumor with Flexynesis Final - Survival Markers of Lower Grade Oct 5, 2026 1 ✅ ✅ ❌
Modeling Breast Cancer Subtypes with Flexynesis Final - Modeling Breast Cancer Subtypes (flexynesis only) Oct 5, 2026 3 ✅ ✅ ✅
Prepare Data from CbioPortal for Flexynesis Integration Flexynesis - get data from Cbioportal Oct 5, 2026 1 ✅ ✅ ❌
Unsupervised Analysis of Bone Marrow Cells with Flexynesis Final - Unsupervised Analysis of Bone Marrow Cells Oct 5, 2026 1 ✅ ✅ ❌
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy gpu_jupytool Oct 5, 2026 1 ❌ ❌ ❌
Age prediction using machine learning Age Prediction DNA Methylation Oct 5, 2026 6 ❌ ❌ ❌
Age prediction using machine learning Age Prediction RNA-Seq Oct 5, 2026 6 ❌ ❌ ❌
Basics of machine learning Machine Learning Oct 5, 2026 5 ✅ ❌ ❌
Classification in Machine Learning ml_classification Oct 5, 2026 3 ✅ ✅ ❌
Clustering in Machine Learning Clustering in Machine Learning Oct 5, 2026 2 ✅ ❌ ❌
Comparing ligand-binding site predictions across different protein structure modalities using SIMORGH simorgh Oct 5, 2026 1 ✅ ✅ ✅
Deep Learning (Part 1) - Feedforward neural networks (FNN) Intro_To_FNN_v1_0_11_0 (imported from uploaded file) Oct 5, 2026 1 ✅ ✅ ❌
Deep Learning (Part 2) - Recurrent neural networks (RNN) Intro_To_RNN_v1_0_10_0 Oct 5, 2026 2 ✅ ❌ ❌
Deep Learning (Part 3) - Convolutional neural networks (CNN) Intro_To_CNN_v1.0.11.0 Oct 5, 2026 1 ✅ ❌ ❌
GLEAM Image Learner - Validating Skin Lesion Classification on HAM10000 Image Learner - HAM10000 Oct 5, 2026 1 ✅ ✅ ✅
GTEx Tissue Modeling with Galaxy Image Learner Image Learner - GTEx Tissue Modeling Oct 5, 2026 1 ✅ ✅ ❌
Galaxy Tabular Learner - Building a Model using Chowell clinical data Ludwig - Image recognition model - MNIST Oct 5, 2026 1 ✅ ❌ ❌
Gleam Multimodal Learner - Head and Neck cancer Recurrence Prediction with HANCOCK GLEAM Multimodal Learner - HANCOCK recurrence prediction Oct 5, 2026 1 ✅ ✅ ❌
Image classification in Galaxy with fruit 360 dataset fruit_360 Oct 5, 2026 2 ❌ ❌ ❌
Interval-Wise Testing for omics data Workflow Constructed From History 'IWTomics Workflow' Oct 5, 2026 5 ❌ ❌ ❌
Introduction to deep learning Intro_To_Deep_Learning Oct 5, 2026 1 ❌ ❌ ❌
Machine learning: classification and regression Classification LSVC Oct 5, 2026 6 ✅ ❌ ❌
Machine learning: classification and regression Regression GradientBoosting Oct 5, 2026 6 ✅ ❌ ❌
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis papaa@0.1.9_PI3K_OG_model_tutorial Oct 5, 2026 1 ❌ ❌ ❌
Regression in Machine Learning ml_regression (imported from uploaded file) Oct 5, 2026 2 ✅ ✅ ❌
Text-mining with the SimText toolset Simtext training workflow Oct 5, 2026 1 ❌ ❌ ❌
Train and Test a Deep learning image classifier with Galaxy-Ludwig Ludwig - Image recognition model - MNIST Oct 5, 2026 2 ✅ ❌ ❌

Your topic's Workflow Testing Results

Coming Soon™

Learning Pathways using materials from this Topic

GTN logo with a multi-coloured star and the words Galaxy Training Network
From R to Machine Learning; an introductory course

This learning path aims to teach you the basics of Machine Learning using R. Initially you will learn how to code in R within the Galaxy platform, gaining some familiarity into how to wrangle and analyze data. Then, you will be guided through the various types of Machine Learning techniques, developing some simple models using R.

beginner statistics data-science
GTN logo with a multi-coloured star and the words Galaxy Training Network
Machine learning

This learning path teaches machine learning from simple concepts to complex ones. You start with a basic introduction to Machine learning. Then you move to different aspects of supervised machine learning such as classification and regression. Further topics introduce you to deep learning concepts such as convolutional and recurrent neural networks and conclue with fine-tuning a large language like model...

beginner machine learning
ELIXIR logo
Artificial Intelligence and Machine Learning in Life Sciences using Python

Artificial intelligence (AI) has permeated our lives, transforming how we live and work. Over the past few years, a rapid and disruptive acceleration of progress in AI has occurred, driven by significant advances in widespread data availability, computing power and machine learning. Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive...

elixir ai ml
Statistics for your Learning Pathways
Stats powered by Plausible Analytics

Events using materials from this Topic

TODO once this is merged: https://github.com/galaxyproject/training-material/pull/4963

Statistics For Your Materials

Stats powered by Plausible Analytics