The ACM KDD 2025 Health Day theme, ''Harnessing AI Opportunities in Biomedicine and Healthcare'' highlights the transformative potential of AI-driven applications in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.

The ACM KDD 2024 Health Day theme, "Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery," highlights the transformative potential of AI-driven ecosystems in healthcare, translational biomedical research, and basic biological research. This extended abstract discusses recent advancements, challenges, and future directions, focusing on integrating AI-ready data sets, interdisciplinary collaborations, and ethical AI practices. It aims to catalyze discussions on the potential of AI ecosystems in revolutionizing healthcare and related fields.

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies. However, to our knowledge, there has been no attempt to develop an end-to-end system to annotate, index, or otherwise curate these reports. In this paper, we propose a novel computational resource platform, CREATe, for extracting, indexing, and querying the contents of clinical case reports. CREATe fosters an environment of sustainable resource support and discovery, enabling researchers to overcome the challenges of information science. An online video of the demonstration can be viewed at https://youtu.be/Q8owBQYTjDc.

Motivation: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results: The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation: Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

MOTIVATION: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users' own research presentations and publications. RESULTS: For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook-style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like 'mitochondrion' or 'receptor' are available as a library of graphic elements. Detailed lower-level diagrams are now downloadable in editable PPTX format as sets of interconnected objects. AVAILABILITY AND IMPLEMENTATION: http://reactome.org. CONTACT: [email protected] or [email protected].

SUMMARY: BioJS is an open-source project whose main objective is the visualization of biological data in JavaScript. BioJS provides an easy-to-use consistent framework for bioinformatics application programmers. It follows a community-driven standard specification that includes a collection of components purposely designed to require a very simple configuration and installation. In addition to the programming framework, BioJS provides a centralized repository of components available for reutilization by the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: http://code.google.com/p/biojs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.