Statistics and machine learning

Statistical Analyses for omics data and machine learning using Galaxy tools

Material

You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides () and tutorial () buttons below.

Machine Learning

Tutorials introducing fundamental concepts and techniques, guiding learners through data preprocessing, model training, evaluation, and application.

Lesson Slides Hands-on Recordings Input dataset Workflows
Foundational Aspects of Machine Learning using Python
elixir ai-ml jupyter-notebook

Generative Artificial Intelligence and Large Langage Model

Tutorials covering the creation, pretraining, and applications of Generative Artificial Intelligence and Large Language Models.

Lesson Slides Hands-on Recordings Input dataset Workflows
Pretraining a Large Language Model (LLM) from Scratch on DNA Sequences
elixir ai-ml Large Language Model jupyter-notebook
Fine-tuning a LLM for DNA Sequence Classification
elixir ai-ml Large Language Model jupyter-notebook
Predicting Mutation Impact with Zero-shot Learning using a pretrained DNA LLM
elixir ai-ml Large Language Model jupyter-notebook
Generating Artificial Yeast DNA Sequences using a DNA LLM
elixir ai-ml Large Language Model jupyter-notebook

Flexynesis - multi-omics Integration

Tutorials covering the integration of multi-omics data using advanced statistical and machine learning techniques with Flexynesis package.

Lesson Slides Hands-on Recordings Input dataset Workflows
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

Other

Assorted Tutorials

Lesson Slides Hands-on Recordings Input dataset Workflows
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy
interactive-tools machine-learning deep-learning jupyter-lab image-segmentation protein-3D-structure
Age prediction using machine learning
Basics of machine learning
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
interactive-tools machine-learning deep-learning jupyter-lab fine-tuning dephosphorylation-site-prediction
GLEAM Image Learner - Validating Skin Lesion Classification on HAM10000
HAM10000 Dataset Image Classification Deep Learning Image Learner Skin Lesion Classification
Galaxy Tabular Learner - Building a Model using Chowell clinical data
LORIS Score Model Machine Learning Tabular Learner
Gleam Multimodal Learner - Head and Neck cancer Recurrence Prediction with HANCOCK
Multimodal Learning GLEAM HANCOCK Dataset Recurrence Prediction
Image classification in Galaxy with fruit 360 dataset
Interval-Wise Testing for omics data
Introduction to Machine Learning using R
interactive-tools
Introduction to deep learning
Machine learning: classification and regression
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis
Machine learning Pan-cancer cancer biomarkers oncogenes and tumor suppressor genes
Regression in Machine Learning
Supervised Learning with Hyperdimensional Computing
Text-mining with the SimText toolset
interactive-tools
Train and Test a Deep learning image classifier with Galaxy-Ludwig
MNIST Deep learning Ludwig
Regulations/standards for AI using DOME
elixir ai-ml

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Editorial Board

This material is reviewed by our Editorial Board:

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Contributors

This material was contributed to by:

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Funding

These individuals or organisations provided funding support for the development of this resource

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Gallantries
This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.
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de.NBI
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ELIXIR Europe
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UFR
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EuroScienceGateway
EuroScienceGateway was funded by the European Union programme Horizon Europe (HORIZON-INFRA-2021-EOSC-01-04) under grant agreement number 101057388 and by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee grant number 10038963.