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
AOPxGeneNet: an AOP-guided graph integration framework for transcriptomic network interpretation and topology-constrained mechanistic modeling
Transcriptomic studies can reveal extensive molecular changes associated with chemical exposure, disease, or biological perturbation. However, translating these changes into interpretable mechanistic hypotheses remains challenging. Conventional approaches such as differential expression, pathway enrichment, and gene co-expression analysis identify relevant genes and biological processes, but often provide limited information about how these observations connect to directed biological events, disease progression, or adverse outcomes.
AOPxGeneNet is a mechanistic evidence-integration framework that connects transcriptomic observations with structured knowledge from the Adverse Outcome Pathway framework. It combines gene co-expression networks, gene-to-Key Event mappings, directed AOP topology, evidence-aware prioritization, heterogeneous graph representation, and topology-constrained modelling to support transparent interpretation of molecular data. A central feature of the framework is the preservation of relationship semantics, distinguishing statistical gene–gene associations from mechanistic gene–Key Event mappings and directed Key Event Relationships.
The session will begin with a presentation introducing the scientific motivation, conceptual foundations, analytical modules, and potential applications of AOPxGeneNet in toxicology, disease research, biomarker prioritization, and mechanistic hypothesis generation. Particular attention will be given to how AOPxGeneNet extends conventional transcriptomic analysis by identifying genes and Key Events with strong mechanistic support within directed AOP networks.
The presentation will be followed by a guided practical tutorial in which participants will run an AOPxGeneNet workflow using a prepared transcriptomic dataset. Participants will explore co-expression modules, apply AOPxLink to prioritize mechanistically relevant genes and Key Events, construct and inspect an integrated evidence graph, trace potential pathways from molecular observations toward adverse outcomes, and interpret the resulting mechanistic hypotheses.
By the end of the session, participants will understand the principles of topology-aware transcriptomic interpretation and will have practical experience using AOPxGeneNet to transform transcriptomic results into structured, traceable, and biologically meaningful mechanistic evidence.