Asmaa Ali is an Associate Product Owner for Data, Knowledge, and Safe and Sustainable by Design platforms at Edelweiss Connect GmbH, where she works at the intersection of artificial intelligence, knowledge graphs, bioinformatics, and mechanistic toxicology. She specialises in developing data- and knowledge-driven platforms that connect biological evidence, computational models, and toxicological knowledge to support chemical safety assessment. Her work focuses particularly on Adverse Outcome Pathways, mechanistic transcriptomics, biomedical knowledge graphs, predictive toxicology, and AI-assisted evidence integration.
In her current role, Asmaa contributes to the scientific and product development of platforms for Next Generation Risk Assessment and Safe and Sustainable by Design. She works closely with researchers, developers, designers, and domain experts to translate scientific requirements into practical, reproducible, and user-centred digital solutions. Her work includes AOPGraphExplorer, an evidence-aware platform for exploring mechanistic AOP knowledge, and AOPxGeneNet, a framework for integrating transcriptomic networks with structured biological and toxicological knowledge.
With an academic background in computer science and bioinformatics, Asmaa combines expertise in machine learning, graph-based methods, software development, data management, and biological data analysis. Her wider project experience includes chemical toxicity prediction, aquatic toxicology, nanomaterial characterisation, scientific image analysis, CYP450 and ADMET modelling, and the application of language models to chemical risk assessment.
Previously, she worked at the Egypt Center for Research and Regenerative Medicine, where she optimised genomics pipelines and contributed to the Egyptian Genome Project. At Rosettastein Consulting GmbH, she developed machine-learning models for chemical toxicity prediction. She has also contributed to bioinformatics education and led collaborative AI initiatives, including the OpenTox AI Hackathon 2023.
Asmaa’s professional focus is the development of AI-enabled knowledge infrastructures for mechanistic toxicology and chemical safety. Through this work, she aims to make complex scientific evidence more structured, transparent, interpretable, and actionable for researchers and decision-makers.
ORCID: https://orcid.org/0000-0001-9795-3489
GitHub: https://github.com/asmaa-a-abdelwahab
OpenTox Summer School 2026
AOPGraphExplorer: An Evidence-Aware Knowledge Infrastructure for Multi-Domain Mechanistic Exploration of Adverse Outcome Pathways
Adverse Outcome Pathways (AOPs) provide a structured framework for organizing mechanistic knowledge from molecular initiating events through key events to adverse outcomes. However, practical reuse of AOP knowledge can be challenging when information is distributed across individual AOP-Wiki pages, supporting annotations, evidence fields, and external biological resources. This practical session will introduce AOPGraphExplorer, an evidence-aware graph-based platform designed to support interactive exploration, interpretation, and reuse of AOP networks.
The session will begin with a short presentation introducing the motivation behind AOPGraphExplorer, its graph-based representation of AOP-Wiki knowledge, and its use for multi-domain mechanistic analysis. Participants will learn how Molecular Initiating Events, Key Events, Key Event Relationships, and Adverse Outcomes are represented as causal graph elements, while biological processes, pathways, genes, proteins, chemicals, anatomy, diseases, phenotypes, and applicability information are added as contextual annotation layers.
The hands-on tutorial will guide participants through the main functionalities of the platform, including AOP-, Key Event-, and keyword-based searches; evidence-aware filtering using weight-of-evidence and quantitative understanding descriptors; annotation-driven graph refinement; interactive network visualization; network statistics; and export of reusable HTML and JSON outputs. Participants will also explore how the graph-grounded AI companion can support interpretation by combining extracted graph information with retrieved AOP-Wiki evidence while clearly distinguishing graph-derived observations, supporting evidence, mechanistic interpretation, and remaining uncertainties.
Using a Parkinson’s disease case study, attendees will examine how multiple AOPs can be integrated into a connected mechanistic network to reveal shared biological mechanisms such as mitochondrial dysfunction, oxidative stress, calcium imbalance, proteostasis disruption, and dopaminergic neurodegeneration. By the end of the session, participants will be able to use AOPGraphExplorer to explore AOP networks, identify mechanistic connections, detect evidence gaps, interpret biological context, and generate reusable outputs for toxicology, pharmacology, and risk assessment applications.