ISMB/ECCB 2024 Meeting

Dr. Mayla R. Boguslav

Title: Learning from our collective scientific ignorance: How can ontologies help us determine what isn't yet?

Abstract:

Ontologies beg the question what is or exists (known knowns). I seek to determine what isn't or doesn't exist yet (known unknowns or questions). Ontologies aim to make knowledge accessible, transparent, and searchable. Biological ontologies define the entities and relations in biological domains. The community seeks to organize, present, and disseminate knowledge in biomedicine and the life sciences more generally. This can also be done for our collective scientific ignorance - our missing or incomplete knowledge. Let's make our collective scientific ignorance accessible, transparent, and searchable. In fact, research begins with a question and progresses by exploring new and uncharted territory. Enumerating what we don't know yet can help students, researchers, funders, and publishers generate novel research questions, prioritize resources, and rebuild trust in science. Further, ideally, we combine both knowledge and ignorance to determine solved and unsolved questions. I will present my ignorance taxonomy and ignorance-base (comparable to a knowledge-base) that used ontologies. More generally, I will present a new scientific method framework that shifts the focus to ignorance and questions, not just knowledge. Join me to talk about what we don’t know yet. 

Biography: 

Mayla R. Boguslav seeks to determine what isn't (yet). She works to uncover our collective scientific questions that are not yet answered by creating taxonomies and using ontologies. Focusing on what science has yet to answer helps researchers locate the role of their ongoing work, clarifies what scientists are saying and what they are not, and rebuilds trust in science. Mayla is a Postdoctoral Fellow in Mathematics at Colorado State University. She received the 2023 AMIA Edward H. Shortliffe Doctoral Dissertation Award for her PhD in Computational Biosciences from the University of Colorado Anschutz Medical Campus.

Dr. Karin Slater

Title: Exploring Multiple Perspectives for Associative Knowledgebases

Abstract: 

Databases encoding associative relationships between biomedical entities function as background knowledge that are leveraged for a range of purposes. For example, disease-phenotype associations are used for differential diagnosis and variant prediction, while gene-function associations are used in gene set enrichment analyses. 

In the ontology world, these associative knowledgebases lie somewhere between the conceptualisation and instance spaces, defining foundational knowledge that is often probabilistic, associative, or uncertain, rather than axiomatic. They are formed through some combination of manual curation from expert knowledge, experimental data, and analysis of co-occurrence in literature text. Due to this aetiology of associations, existing databases represent a particular perspective on biomedical knowledge, and it is one that differs from those that might be cultivated from analysis of other sources, such as clinical data, public discussion, or alternative modularisations of literature text. 

We will explore the similarities and differences between associative knowledgebases derived from these contexts, including methodological concerns, hypothesis generation, characterisation, and implications for downstream applications.

Biography: 

Dr. Slater is an Assistant Professor of Biomedical Semantics at the University of Birmingham, working in the Centre for Health Data Science and Centre for Environmental Research and Justice. With applications spanning healthcare, chemical risk assessment, and radiation biology, she is interested in multi-contextual representations of biomedical entities and developing practical methods for discerning actionable insights from data.