Statistics and machine learning — Editorial Board Home

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Editorial Board

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Topic Materials

Material Contributions v2 Pre-requisites Follow up trainings Data on Zenodo Notebook Server Compatibility
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 ✅ ✅ ✅ ✅
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy ✅ ✅ ✅
Age prediction using machine learning ✅ ✅
Basics of machine learning ✅ ✅
Building the LORIS LLR6 PanCancer Model Using PyCaret ❌ ✅
Classification in Machine Learning ✅ ✅
Clustering in Machine Learning ✅ ✅
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 ✅ ✅ ✅
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 Differences in Chinese Newspaper Articles ✅ ✅
Text-mining with the SimText toolset ❌ ✅
Train and Test a Deep learning image classifier with Galaxy-Ludwig ❌ ✅
Foundational Aspects of Machine Learning using Python ✅ ✅ ✅ ✅
Neural networks using Python ✅ ✅ ✅ ✅
Deep Learning (without Generative Artificial Intelligence) using Python ✅ ✅ ✅ ✅
Regulations/standards for AI using DOME ✅ ✅

Topic Workflows

Material Workflow Updated Version Tests Reports Comments
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy gpu_jupytool Apr 26, 2025 1 ❌ ❌ ❌
Age prediction using machine learning Age Prediction DNA Methylation Apr 26, 2025 6 ❌ ❌ ❌
Age prediction using machine learning Age Prediction RNA-Seq Apr 26, 2025 6 ❌ ❌ ❌
Basics of machine learning Machine Learning Apr 26, 2025 5 ✅ ❌ ❌
Building the LORIS LLR6 PanCancer Model Using PyCaret Ludwig - Image recognition model - MNIST Apr 26, 2025 1 ✅ ❌ ❌
Classification in Machine Learning ml_classification Apr 26, 2025 3 ✅ ✅ ❌
Clustering in Machine Learning Clustering in Machine Learning Apr 26, 2025 2 ✅ ❌ ❌
Deep Learning (Part 1) - Feedforward neural networks (FNN) Intro_To_FNN_v1_0_10_0 Apr 26, 2025 1 ✅ ❌ ❌
Deep Learning (Part 2) - Recurrent neural networks (RNN) Intro_To_RNN_v1_0_10_0 Apr 26, 2025 2 ✅ ❌ ❌
Deep Learning (Part 3) - Convolutional neural networks (CNN) Intro_To_CNN_v1.0.11.0 Apr 26, 2025 1 ✅ ❌ ❌
Image classification in Galaxy with fruit 360 dataset fruit_360 Apr 26, 2025 2 ❌ ❌ ❌
Interval-Wise Testing for omics data Workflow Constructed From History 'IWTomics Workflow' Apr 26, 2025 5 ❌ ❌ ❌
Introduction to deep learning Intro_To_Deep_Learning Apr 26, 2025 1 ❌ ❌ ❌
Machine learning: classification and regression Classification LSVC Apr 26, 2025 6 ✅ ❌ ❌
Machine learning: classification and regression Regression GradientBoosting Apr 26, 2025 6 ✅ ❌ ❌
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis papaa@0.1.9_PI3K_OG_model_tutorial Apr 26, 2025 1 ❌ ❌ ❌
Regression in Machine Learning ml_regression Apr 26, 2025 1 ✅ ❌ ❌
Text-Mining Differences in Chinese Newspaper Articles Text-Mining Differences in Chinese Newspaper Articles Apr 26, 2025 2 ❌ ✅ ✅
Text-mining with the SimText toolset Simtext training workflow Apr 26, 2025 1 ❌ ❌ ❌
Train and Test a Deep learning image classifier with Galaxy-Ludwig Ludwig - Image recognition model - MNIST Apr 26, 2025 2 ✅ ❌ ❌

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Learning Pathways using materials from this Topic

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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
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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
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Events using materials from this Topic

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

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