This tutorial introduces participants to the integration of multiomics data from genomics, proteomics, transcriptomics, and metabolomics, focusing on computational approaches to uncover hidden relationships between biological entities. The session will cover techniques such as Non-negative Matrix Factorization (NMF), machine learning, and Graph Neural Networks (GNNs) to model multi-layered biological interactions and predict biological outcomes such as disease classification, drug responses, and biomarker discovery. Attendees will gain hands-on experience in processing and analyzing real-world multiomics datasets using open-source tools such as Python, pandas, scikit-learn.

This tutorial provides a comprehensive overview of machine learning techniques applied to drug response prediction on cancer cell lines, with a focus using Graph Neural Networks (GNNs) and Gaussian processes (GPs). Participants will gain both theoretical knowledge and practical experience through interactive lectures and hands-on demonstrations.

This tutorial introduces Simulation-Based Inference (SBI), a framework combining Bayesian modeling, AI techniques, and high-performance computing (HPC) to address key challenges in computational biology, such as performing reliable inference with limited data by using AI-based approximate Bayesian computation. Moreover, it tackles the problem of intractable likelihood functions, thereby allowing to utilize Bayesian inference for biological systems with multiple sources of stochasticity. The tutorial also demonstrates how to leverage HPC environments to drastically reduce inference runtimes, making it highly relevant for large-scale biological problems. This tutorial bridges theoretical foundations with hands-on applications in computational biology. Participants will learn to implement SBI frameworks using diverse biological models, such as molecular dynamics simulations, agent-based tumor growth models, count data modeling, and Lotka-Volterra systems. Practical exercises in Jupyter notebooks guide attendees through SBI workflows, from simple coin-flipping examples to more complex biological simulations, ensuring accessibility for participants with varied backgrounds. The tutorial’s inclusion of cutting-edge methods like Sequential Neural Posterior Estimation and its emphasis on parallelization and HPC scalability align closely with the scientific community's focus on innovation in computational biology. A previous iteration of the tutorial at the Helmholtz AI Conference 2024 received excellent reviews and led to interdisciplinary discussions, highlighting its broad applicability and impact. For this conference, the content has been further refined with additional examples relevant to the community, ensuring it meets the needs of bioinformatics researchers.

Agentic workflow is a process of interacting with Large Language Models (LLMs) to complete complex tasks - allowing practitioners to build pipelines that integrate data retrieval, reasoning, and execution steps. This tutorial will guide participants through the conceptual and practical foundations of setting up their own agentic workflows. By combining prompt engineering techniques, retrieval-augmented generation tool use and deployment strategies that safeguard data privacy, tutorial participants will learn how to build, deploy and tune their own personal copilots for use in bioinformatics workflows.

The capabilities of agentic workflows—driven by improving LLMs —are rapidly expanding, while cloud offerings are making these advanced computational tools more accessible than ever before. By integrating agentic workflows into bioinformatics pipelines, practitioners can significantly reduce their time-to-analysis. Lowering the barrier to entry for novices and allowing expert practitioners to scale their work with greater efficiency, these workflows democratize cutting-edge computational methods and ensure that the tutorial participants can capitalize and leverage the latest advances in their work and careers in general. This tutorial will integrate state-of-the-art prompting techniques, retrieval augmentation strategies, add context to model selection and explore the fundamentals between choosing amongst the different techniques and current trends.

The African Genomics Data Hub (AfriGen-D) provides essential resources for analyzing African genetic data, addressing unique challenges posed by the continent's exceptional genetic diversity. This hands-on tutorial focuses on genotype imputation and downstream analysis using AfriGen-D resources.

Through practical exercises, participants will master data quality control specific to African genetic data, execute imputation and basic GWAS workflows, and learn to interpret results using the AfriGen-D Imputation Service, African Genomics Medicine Portal (AGMP), and African Genomics Variation Database (AGVD).

Enhance your African genomics research capabilities with this practical tutorial. Using AfriGen-D resources, learn to prepare data, perform genotype imputation, conduct basic GWAS analysis, and interpret results with tools optimized for African genetic diversity.

COBRApy is a user-friendly open source Python package that makes learning this modeling accessible and convenient. A hands-on workshop, which included exercises and problem-solving, would introduce participants to this technique. By bringing together researchers interested in the development of this type of modeling, this type of workshop would not only teach a valuable skill, but also encourage the development of new collaborations. The practical skills participants acquire can immediately be applied to their research, deepening knowledge and accelerating discoveries.