Artificial Intelligence and Machine Learning in Life Sciences using Python

PURL: https://gxy.io/GTN:P00029
Comment: What is a Learning Pathway?
A graphic depicting a winding path from a start symbol to a trophy, with tutorials along the way
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

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 volumes of unlabelled data. Moreover, given the large amounts of omics data that are being generated and made accessible to researchers due to the drop in the cost of high-throughput technologies, analysing these complex high-volume data is not trivial, and the use of classical statistics can not explore their full potential. As such, Machine Learning (ML) and Artificial Intelligence (AI) have been recognized as key opportunity areas for ELIXIR, as evidenced by a number of ongoing activities and efforts throughout the community. However, beyond the technological advances, it is equally important that the individual researchers acquire the necessary knowledge and skills to fully take advantage of Machine Learning. Being aware of the challenges, opportunities and constraints that ML applications entail, is a critical aspect in ensuring high quality research in life sciences

Module 0: Python warm-up

Python warm-up for statistics and Machine Learning

Time estimation: 6 hours

Learning Objectives
  • Learn the fundamentals of programming in Python
  • to do
Lesson Slides Hands-on Recordings
Introduction to Python

Module 1: Foundational Aspects of Machine Learning

Foundational Aspects of Machine Learning

Time estimation: 3 hours

Learning Objectives
  • general sklearn syntax intro
  • overfit/underfit
  • the need for regularization
  • cross validation and a test set
  • metrics and imbalance
Lesson Slides Hands-on Recordings

Module 2: Neural networks

Neural networks

Time estimation: 3 hours

Learning Objectives
  • Initializing model with a single layer (code)
  • Loss function
  • Model as equation
  • How model parameters are learned
  • Training steps (code)
  • Predictions and save+load models
  • Initializing model with multiple layers (code)
  • Forward step
  • Concept of backprop and epochs
  • Training (code)
Lesson Slides Hands-on Recordings

Module 3: Deep Learning (without Generative Artificial Intelligence)

Deep Learning (without Generative Artificial Intelligence)

Time estimation: 3 hours

Learning Objectives
  • Input data representation
  • Concept of filters
  • Concept of pooling layers
  • Initialising a model with conv layers (code)
  • Concept of RNNs
  • Concept of attention
  • Implementation of RNN (code)
  • Implementation of attention mechanism (code)
  • Implementation of fine-tuning (code)
Lesson Slides Hands-on Recordings

Module 4: Generative Artificial Intelligence and Large Langage Model

Generative Artificial Intelligence and Large Langage Model using Python

Time estimation: 3 hours

Learning Objectives
  • pretraining LLM for DNA
  • finetuning LLM
  • zeroshot prediction for DNA variants and synthetic DNA sequence generation.
Lesson Slides Hands-on Recordings

Module 5: Regulations/standards for AI using DOME

Regulations/standards for AI using DOME

Time estimation: 3 hours

Learning Objectives
  • to do
Lesson Slides Hands-on Recordings

Editorial Board

This material is reviewed by our Editorial Board:

orcid logoBérénice Batut avatar Bérénice Batut