Workshop Schedule

09:00 - 09:30          Opening : Dr Ali Hasnain (Assistant Professor in Health Data Analytics)

09:30- 10:25 Keynote Speech : Michel Dumontier (Distinguished Professor of Data Science at Maastricht University)

Towards Biomedical Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions 

The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on how data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.

  In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine. 

Bio

Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data. 

10:30 – 11:00 Coffee Break

11:00 - 12:30

Session 1: Healthcare and Life Sciences Knowledge Graphs

• Integrating Multi-Modal Spatial Data using Knowledge Graphs – a Case Study of Microflora Danica.

Mads Corfixen, Thomas Heede, Tomer Sagi, Mads Albertsen, Thomas D. Nielsen and Katja Hose. 

• Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction .

Rita T. Sousa and Heiko Paulheim. 

12:30 – 14:00 Lunch Break

14:00 - 14:30 Session 2: Artificial Intelligence and Healthcare and Life Sciences Knowledge Graphs

• Exploring the potential of Artificial Intelligence based Chatbots for generating Federated SPARQL queries over Bioinformatics Knowledge Graphs . 

Sourav Maiti, Qurratal Ain Fatimah, Syeda Mah E Fatima and Ali Hasnain.

14:30 - 15:30 Session 3: Text, Data and Ontologies in Healthcare and Life Sciences 

• Modelling the EGFR through the Protein Conformation Ontology . Giacomo De Colle, Morgan Mitchell and Alexander D. Diehl. 

• Estimating the Quality of Translated Medical Texts using Back Translation & Resource Description Framework . Vinay Neekhra and Dipti Misra Sharma. 

15:30 – 15:40 Closing

15:40 – 16:00 Coffee Break