layout: learning-pathway type: use cover-image: shared/images/elixir.png cover-image-alt: ELIXIR logo

editorial_board:

• bebatut

title: Artificial Intelligence and Machine Learning in Life Sciences using Python description: | 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 tags: [elixir, ai, ml]

pathway:

• section: "Module 0: Python warm-up" description: Python warm-up for statistics and Machine Learning tutorials:

  • name: python-basics topic: data-science

  • name: python-warmup-stat-ml topic: data-science

• section: "Module 1: Foundational Aspects of Machine Learning" description: Foundational Aspects of Machine Learning tutorials:

  • name: intro-to-ml-with-python topic: statistics

• section: "Module 2: Neural networks" description: Neural networks tutorials:

  • name: neural-networks-with-python topic: statistics

• section: "Module 3: Deep Learning (without Generative Artificial Intelligence)" description: Deep Learning (without Generative Artificial Intelligence) tutorials:

  • name: deep-learning-without-gai-with-python topic: statistics

• section: "Module 4: Generative Artificial Intelligence and Large Langage Model for Genomics using Python" description: This tutorial series provides a comprehensive guide to leveraging large language models for genomics, covering pretraining, fine-tuning, mutation impact prediction, sequence generation, and optimization. tutorials:

  • name: genomic-llm-pretraining topic: statistics

  • name: genomic-llm-finetuning topic: statistics

  • name: genomic-llm-zeroshot-prediction topic: statistics

  • name: genomic-llm-sequence-generation topic: statistics

  • name: genomic-llm-sequence-optimization topic: statistics

• section: "Module 5: Regulations/standards for AI using DOME" description: Regulations/standards for AI using DOME tutorials:

  • name: dome topic: statistics