Statistics and machine learning

Statistical Analyses for omics data and machine learning using Galaxy tools

Requirements

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Material

You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides () and tutorial () buttons below.
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
Building the LORIS LLR6 PanCancer Model Using PyCaret
LORIS Score Model Machine Learning 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
interactive-tools machine-learning deep-learning jupyter-lab fine-tuning dephosphorylation-site-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

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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.