Asmaa Ali is a Research Data Scientist at Edelweiss Connect GmbH and a SaferWorldbyDesign Fellow, specialising in integrating artificial intelligence, data management, and toxicology to advance chemical risk assessment. With a solid academic foundation in Computer Science and Bioinformatics, she leverages her expertise in software development, machine learning, knowledge graphs, and data management to develop predictive models and innovative solutions for this critical field.
Currently, Asmaa is contributing to impactful projects, including toxicity prediction in aquatic species, data management and image analysis for nanomaterials characterization, and the application of language models to enhance chemical risk assessment practices.
Her professional journey spans several institutions. At the Egypt Center for Research and Regenerative Medicine, she optimised genomics pipelines and contributed to the Egyptian Genome Project. At Rosettastein Consulting GmbH, she developed machine learning models for chemical toxicity prediction. Asmaa has also made significant contributions to academia, serving as a teaching assistant for bioinformatics courses and leading AI-focused initiatives such as the OpenTox AI Hackathon 2023.
Passionate about the intersection of technology and science, Asmaa envisions a future where AI and precision toxicology converge to foster healthier lives and environments.
ORCID: https://orcid.org/0000-0001-9795-3489
Github: https://github.com/asmaa-a-abdelwahab
AOPGraphExplorer 2.0: An Integrated Graph-Based Platform for Multi-Domain Annotation and Visualization of Adverse Outcome Pathways
Asmaa A. Abdelwahab1,2, Barry Hardy1,2
1 OpenTox Association, Basel, Switzerland
2 Edelweiss Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C, 4057 Basel, Switzerland
Abstract
The Adverse Outcome Pathway (AOP) framework is a cornerstone in mechanistic toxicology, linking molecular initiating events to adverse biological outcomes. Yet, the increasing complexity and fragmentation of AOP-related data across multiple resources hinder efficient exploration and mechanistic interpretation.
Here, we present AOPGraphExplorer 2.0, an integrated graph platform that enables the interactive visualization, annotation, and analysis of AOP networks derived from AOPWiki. The new version introduces seamless integration of multi-domain annotation sources, allowing users to enrich AOP networks with pathway, disease, and tissue-specific information. This integration facilitates a deeper understanding of how molecular perturbations propagate across biological systems to drive toxicological and pathological outcomes.
AOPGraphExplorer 2.0 provides instant, graph-based visualizations with exportable HTML and JSON formats for documentation, sharing, and computational reuse. It also delivers quantitative network statistics, summarizing nodes, edges, AOPs, stressors, and annotation coverage, alongside evidence-based filtering of Key Events (KEs) by confidence and quantitative understanding scores.
By bridging AOPWiki with external biomedical knowledge, AOPGraphExplorer 2.0 transforms AOP exploration into a multi-domain network analysis process, advancing the integration of systems toxicology, pharmacology, and disease biology. This user-friendly, open-access platform supports hypothesis generation, data interpretation, and evidence-based decision-making, accelerating research in drug safety, environmental health, and risk assessment.