• Adam Talbot, Adam Talbot, Seqera
• Geraldine Van der Auwera, Geraldine Van der Auwera, Seqera

Turning a promising research project into a robust, real-world solution requires tools that support both early experimentation and enterprise-scale deployment. When reproducibility and reliability are non-negotiable, you need a platform that's flexible during ideation and powerful enough to meet the demands of mega-scale computation and collaborative research.

Too often, scaling up means switching tools, rewriting pipelines, or reprovisioning infrastructure — an expensive, frustrating process that can introduce errors and undermine scientific reproducibility.

In this talk, we will explore how Seqera's integrated suite of products empowers you to scale and accelerate your scientific research.

• Viktor Rudko, Viktor Rudko, Forschungszentrum Jülich GmbH
• Peter Belmann, Peter Belmann, Forschungszentrum Jülich GmbH
• Nils Hoffmann, Nils Hoffmann, Forschungszentrum Jülich GmbH
• David Weinholz, David Weinholz, Forschungszentrum Jülich GmbH
• Alexander Sczyrba, Alexander Sczyrba, Forschungszentrum Jülich

SimpleVM empowers life science researchers to harness cloud resources, regardless of their expertise in cloud computing. As a multi-cloud application, SimpleVM is optimized for seamless integration with multiple OpenStack® installations. From an OpenStack administrator's perspective, all that is required is an OpenStack project without the need for additional admin privileges. SimpleVM features enhanced security components that scan connection attempts to virtual machines and automatically block suspicious access attempts.

By combining KeyCloak with a Django-based service layer, SimpleVM provides comprehensive user management and customizable role-based access control.
This facilitates the integration of Authentication and Authorisation Infrastructure (AAI) for seamless use of various Identity Providers (IDPs), including LifeScience AAI or a local university IDP. 

In addition to the straightforward launch of virtual machines, the emphasis is placed on advanced features that are intended to streamline and enhance the user experience when working with cloud resources. For example, Virtual Research Environments (VREs) can be deployed with just a few clicks, providing access to powerful applications such as Visual Studio Code(R) or RStudio directly from the browser.
 
The SimpleVM workshop mode fosters the realization of high-attendance teaching sessions. Workshop instructors can easily pre-configure, launch and assign machines to attendees in no time. 
Finally, SimpleVM improves the use of cloud resources with features such as the auto-scalable SLURM clusters.

• Mitchell Shiell, Mitchell Shiell, Ontario Institute of Cancer Research (OICR)
• Melanie Courtot, Melanie Courtot, Ontario Institute for Cancer Research
• Brandon Chan, Brandon Chan, Ontario Institute for Cancer Research (OICR)
• Jon Eubank, Jon Eubank, Ontario Institute for Cancer Research (OICR)
• Robin Haw, Robin Haw, OICR
• Justin Richardsson, Justin Richardsson, Ontario Institute for Cancer Research (OICR)
• Leonardo Rivera, Leonardo Rivera, Ontario Institute for Cancer Research (OICR)
• Lincoln Stein, Lincoln Stein, Ontario Institute for Cancer Research
• Overture Team, Overture Team, Ontario Institute of Cancer Research (OICR)

Overture is used to build platforms that enable researchers to organize and share their data quickly, flexibly and at multiple scales. While Overture successfully powers major international platforms like ICGC-ARGO (100,000+ participants) and VirusSeq (500,000+ genomes), smaller teams generating massive data face prohibitive technical requirements during implementation. How then can we enable teams to build their data platform efficiently and with fewer resources? Prelude addresses this challenge by breaking down platform development into incremental phases, reducing the technical overhead during development and allowing teams to systematically verify requirements through hands-on testing, gaining insights into workflows, data needs, and platform fit.

Prelude guides teams through three progressive phases of data platform development each building upon the previous one's foundation:

- Phase one focuses on data exploration and theming, enabling teams to visualize
and search their data through a customizable UI;

- Phase two expands capabilities to enable tabular data management and validation
with persistent storage;

- Phase three adds file management and object storage.

These phases are supported by comprehensive documentation, deployment automations, and utilities that generate key configuration files, reducing unnecessary time spent on tedious manual configurations.

Prelude represents a practical step toward making data platform development accessible to all teams of all sizes. By providing a widely accessible platform we hope to encourage community requests and feedback such that we can improve and iterate on the platform making it the best it can be for advancing data sharing and reuse across the scientific community.

• Filippo Utro, Filippo Utro, IBM

"The advent of foundation models (FM) and quantum computing
(QC) has ushered in a new paradigm for tackling complex
problems, igniting significant interest across diverse
sectors, particularly within healthcare and life sciences.
This talk will provide an exploration of the latest efforts
at IBM Research dedicated to leveraging FM and QC for
accelerating discovery in healthcare and life sciences. The
discussion will span a range of applications in omics data,
clinical trials, and drug discovery. Finally, in this
presentation, I will discuss technical challenges,
envisioning the new era of FM and QC in healthcare and life
sciences."

• Dorota Latek, Dorota Latek, University of Warsaw

GPCRVS represents an efficient, simple, easily accessible, and open-source web service that, as a decision support system, aims to facilitate the preclinical testing of drug candidates targeting peptides and small protein-binding G protein-coupled receptors. There are three major areas of drug discovery that GPCRVS could facilitate: prediction of drug selectivity, prediction of drug efficacy approximated by Autodock Vina docking scores, or by activity class assigned by the TensorFlow multiclass classifier, or by pChEMBL predictions using the LightGBM regressor, and finally prediction of the drug binding mode, showing the most crucial amino acids involved in the drug-receptor interactions. A comparison with precomputed results for known active compounds enables the prioritization of drug candidates, thereby significantly reducing the cost and length of experimental screening.
In addition, a novel approach to using peptide ligand data sets as SMILES-based fingerprints in conjunction with small-molecule ligand data sets in the training of DNN and GBM models was proposed. This makes possible to benefit from all GPCR-like ligand data sets deposited in ChEMBL, and to design new drugs that could include both peptide and non-peptide scaffolds of increased, unified activity and selectivity. Currently, two groups of peptide/small protein-binding GPCR receptors are included in GPCRVS, allowing it to make comparative predictions for class A and B receptors at the same time. The evaluation of GPCRVS performed using the patent compound data set from Google Patents showed that LightGBM provides the most accurate results among the three classifiers implemented in GPCRVS.

• Supratim Das, Supratim Das, University of Hamburg
• Mahdie Rafiei, Mahdie Rafiei, University of Hamburg
• Andreas Maier, Andreas Maier, University of Hamburg
• Linda Baumbach, Linda Baumbach, University of Hamburg
• Jan Baumbach, Jan Baumbach, University of Hamburg

Prognostic models in healthcare often rely on big data, which is typically distributed across multiple medical cohorts. Even if collected for similar purposes (e.g., capturing symptoms, treatments, and outcomes in osteoarthritis), they frequently differ in acquisition methods, structures, and variable definitions used. These discrepancies impede their integration into a unified, multi-cohort database for joint prognostic model training and pose significant challenges to model transferability, meaning a model trained on one cohort needs to be applied to data of a similar cohort with an incompatible data structure. Current cohort conversion approaches rely on AIs trained on linked, overlapping samples, which many healthcare cohorts lack. Here, we present DB-converter, a self-supervised deep learning architecture leveraging category theory and designed to convert data from different cohorts with different data structures into each other. We demonstrate the power and robustness of the DB-converter using synthetic and real health survey data. Our approach opens new avenues for multi-cohort analyses operating under the assumption that all cohorts to be integrated have been acquired for at least a similar real-world purpose.

• Tim Kucera, Tim Kucera, Max Planck Institute of Biochemistry
• Karsten Borgwardt, Karsten Borgwardt, Max Planck Institute of Biochemistry

We introduce the bioverse, a free and open-source Python package that streamlines biological data preparation for machine learning. Focused on structural biology, it standardizes diverse biomolecular formats for flexible, high-performance workflows. Our demonstration will showcase key features, code examples, and how to launch your own ML projects in minutes.

• Susheel Varma, Susheel Varma, Sage Bionetworks
• Jineta Banerjee, Jineta Banerjee, Sage Bionetworks
• Robert Allaway, Robert Allaway, Sage Bionetworks
• John Hill, John Hill, Sage Bionetworks
• Jay Hodgson, Jay Hodgson, Sage Bionetworks
• Alberto Pepe, Alberto Pepe, Sage Bionetworks
• Christine Suver, Christine Suver, Sage Bionetworks
• Luca Foschini, Luca Foschini, Sage Bionetworks

"The exponential growth of biomedical datasets presents unprecedented opportunities for scientific discovery, yet researchers struggle to find and explore relevant data. Traditional search methods fall short when navigating complex, highly regulated biomedical data repositories. This paper examines these limitations and proposes AI-powered conversational interfaces as a solution.

Key obstacles to effective data discovery include repository fragmentation, inconsistent metadata, vocabulary mismatches, complex search requirements, and inadequate interface design. These challenges are intensified in biomedical research by regulatory restrictions on accessing sensitive data.

Conversational AI systems offer a promising alternative by enabling natural language dialogue with data repositories. Unlike keyword searches, these interfaces understand user intent, ask clarifying questions, and guide researchers to relevant datasets. Synapse.org's experimental chatbot implementation demonstrates how AI-assisted discovery processes complex queries (e.g., ""datasets related to people over 60 with Alzheimer's disease and Type 2 diabetes"") without requiring database expertise. This approach leverages Retrieval-Augmented Generation (RAG) while respecting authorization levels and regulatory compliance.

Such systems facilitate ""metadata spelunking,"" allowing researchers to explore dataset composition, methodology, and potential utility without needing to access sensitive raw data. The paper addresses ethical considerations related to privacy, bias, and trust, while outlining future possibilities for interdisciplinary data discovery.

By bridging the gap between vast biomedical data repositories and researchers, conversational AI interfaces promise to democratize data access, accelerate discovery, and ultimately improve human health."

• Zheng Chen, Zheng Chen, SANKEN
• Ziwei Yang, Ziwei Yang, Bioinformatics Center
• Xihao Piao, Xihao Piao, SANKEN
• Peng Gao, Peng Gao, Institute for Quantitative Biosciences
• Yasushi Sakurai, Yasushi Sakurai, SANKEN
• Yasuko Matsubara, Yasuko Matsubara, SANKEN

In many genomic projects, selecting and preparing assemblies requires complex database queries, manual metadata curation, and bespoke code scripting. We introduce a no-code AI agent workflow that replaces all of these steps with a single plain natural language request. Behind the scenes, five specialized AI agents handle retrieval, quality filtering, ranking, and format conversion. Users receive analysis-ready genome datasets in minutes, freeing them from programming barriers and manual errors so they can focus on biological discovery.

• Prashant Bharadwaj Kalvapalle, Prashant Bharadwaj Kalvapalle, Rice University
• Eddie Kim, Eddie Kim, Rice University
• Marko Tanevski, Marko Tanevski, Rice University
• Sahil Joshi, Sahil Joshi, Rice University
• Benjamin Mao, Benjamin Mao, Rice University
• Anshumali Shrivastava, Anshumali Shrivastava, Rice University
• Todd Treangen, Todd Treangen, Rice University

Omi facilitates bioinformatics analysis by replacing complex command-line processes with a natural language bioinformatics co-pilot. We codify bioinformatics best practices into the LLM to select appropriate pipelines, provide explanations before running them, and return results after pipeline execution. Further democratization through coding bespoke statistical and data visualizations is underway.