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
Regulations/standards for AI using DOME
elixir ai-ml
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
GTEx Tissue Modeling with Galaxy Image Learner
GTEx Tissue Classification Gene Expression Image Learner Deep Learning
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

Learning Pathways

Or have a look at one of our learning pathways involving this topic. Learning pathways are sets of tutorials curated for you by community experts to form a coherent set of lessons around a topic, building up knowledge as you go. We always recommend to follow the tutorials in the order they are listed in the pathway.
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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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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

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Editorial Board

This material is reviewed by our Editorial Board:

Marzia A Cremona avatar Marzia A Cremonaorcid logoFabio Cumbo avatar Fabio Cumboorcid logoAnup Kumar avatar Anup Kumar

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Contributors

This material was contributed to by:

Natalie Kucher avatar Natalie KucherGildas Le Corguillé avatar Gildas Le Corguilléorcid logoAlireza Khanteymoori avatar Alireza KhanteymooriRalf Gabriels avatar Ralf Gabrielsorcid logoMarie Gramm avatar Marie Grammorcid logoArmin Dadras avatar Armin Dadrasorcid logoJunhao Qiu avatar Junhao QiuRaphael Mourad avatar Raphael Mouradorcid logoPaulo Cilas Morais Lyra Junior avatar Paulo Cilas Morais Lyra Juniororcid logoStella Fragkouli avatar Stella FragkouliKhai Van Dang avatar Khai Van DangMélanie Petera avatar Mélanie Peteraorcid logoJeremy Goecks avatar Jeremy GoecksWandrille Duchemin avatar Wandrille Ducheminorcid logoSaskia Hiltemann avatar Saskia Hiltemannorcid logoAlyssa Pybus avatar Alyssa PybusMarzia A Cremona avatar Marzia A Cremonaorcid logoAnup Kumar avatar Anup Kumarorcid logoTeresa Müller avatar Teresa Müllerorcid logoHelena Rasche avatar Helena Rascheorcid logoMichelle Terese Savage avatar Michelle Terese Savageorcid logoDaniel Blankenberg avatar Daniel Blankenbergorcid logoAnthony Bretaudeau avatar Anthony Bretaudeauorcid logoFotis E. Psomopoulos avatar Fotis E. PsomopoulosEkaterina Polkh avatar Ekaterina PolkhDennis Lal group avatar Dennis Lal groupKaivan Kamali avatar Kaivan KamaliVijay avatar Vijayorcid logoBjörn Grüning avatar Björn Grüningorcid logoFabio Cumbo avatar Fabio CumboSimon Bray avatar Simon Brayorcid logoPolina Polunina avatar Polina Poluninaorcid logoNate Coraor avatar Nate CoraorAllissa Dillman avatar Allissa Dillmanorcid logoMartin Čech avatar Martin Čechorcid logoBérénice Batut avatar Bérénice Batutorcid logoAmirhossein Naghsh Nilchi avatar Amirhossein Naghsh Nilchi

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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ELIXIR Europe
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UFR
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de.NBI
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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.