• Robert Hoehndorf
• Tiffany Callahan

• Mayla Boguslav, Mayla Boguslav, Southern California Clinical and Translational Science Institute (USC Keck School of Medicine)

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.

Opportunity for poster presenters to give a brief overview of their work and advertise their upcoming poster session

• Jiyu Chen, Jiyu Chen, The Commonwealth Scientific and Industrial Research Organisation (CSIRO)
• Benjamin Goudey, Benjamin Goudey, The Florey Institute of Neuroscience and Mental Health
• Nicholas Geard, Nicholas Geard, School of Computing and Information Systems
• Karin Verspoor, Karin Verspoor, The RMIT University

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GOA based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. Additionally, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.
We extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.
This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We established a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge.

• Michael Bradshaw, Michael Bradshaw, University of Colorado Boulder
• Ryan Layer, Ryan Layer, University of Colorado Boulder

Knowledge graph embedding (KGE) models have been successfully used for a variety of biomedical applications, but have yet to be effectively applied to rare disease variant prioritization; certain limitations need to be addressed to facilitate application of these models, namely node degree bias. We found there is a cyclical form of bias against under-studied genes and diseases when using KGE models. We found that commonly studied genes–like those related to heretable forms of cancer – perform very well in KGE link prediction tasks (median normalized rank (MNR)=0.91); while less studied genes – like those differentially expressed in females and males, or diseases caused by ancestry specific variations – are deprioritized by the same systems (MNR=0.63-0.71). Our results revealed that not all information contained within large biomedical knowledge graphs is useful for training KGE models. There was a 7-10% improvement in gene-gene edge prediction when the KG was filtered to include only nodes and edges describing genes and diseases. This filtration step also drastically sped up hyperparameter optimization and training times reducing them to 1 - 2.5% that of using the full KG. Several alternative methods for exploring the KG filtration space are explored in this project. Our results show that KGE link prediction performance for gene and disease association is a very nuanced space where careful consideration of the learning model and underlying KG are required. Performance can vary by 5-11% for gene-gene edges and 11-34% for gene-disease predictions depending on the combination of KG and KGE model.

• Azza Althagafi, Azza Althagafi, Computer Science Department
• Robert Hoehndorf, Robert Hoehndorf, King Abdullah University of Science and Technology (KAUST)

Whole-exome and genome sequencing are widely used for diagnosing
patients with rare diseases, but many remain undiagnosed due to
undiscovered disease genes/variants or novel phenotypes arising from
combinations of variants in multiple genes. Interpreting phenotypic
consequences of variants relies on information about gene functions,
expression, and other genomic features. Existing phenotype-based
prioritization methods link molecular features to phenotypic effects
of altering gene functions but are limited by incomplete
gene--phenotype associations and applicability only to genes with
known phenotypes. We developed several computational methods to
prioritize genes based on phenotypes. Our methods incorporate genomic
information, gene functions from the Gene Ontology, anatomical site of
expression from Uberon, celltype of expression using the Cell
Ontology, and clinical phenotypes. We integrate this information and
apply knowledge-enhanced machine learning to prioritize candidate
genes. We apply this work to the prioritization of different types of
genomic variants, including single nucleotide exonic variants,
non-coding variants, and structural variants.
The methods we develop leverage large amounts of background knowledge,
from databases with ontology annotations as well as from ontology
axioms. We evaluated these methods using synthetic and patient-derived
clinical genomes.

• Emily Bordeleau, Emily Bordeleau, University of British Columbia
• Brian Alcock, Brian Alcock, McMaster University
• Andrew McArthur, Andrew McArthur, McMaster University

Multidrug-resistant pathogens continue to challenge aminoglycoside antibiotics with the spread of genetic elements encoding aminoglycoside modifying enzymes (AMEs). Unfortunately, these enzymes have a discordant naming history that further complicates stewardship and surveillance programs. We are undertaking the management and adoption of a single AME nomenclature. We will abide by the guidelines first proposed in 1975 while incorporating additional rules accounting for the scale at which sequencing technology permits AME discovery. Cell-based and biochemical data has been curated from the literature that supports the AMEs characterized to date. CARD will utilize this data to develop software that will guide researchers in the classification of new and existing AME variants. Additionally, CARD will provide tools to evaluate AME benchmarks and recommend available namespace, resolve conflicts, or suggest additional analyses if applicable. After conducting a full review of the AME terminology in the Antibiotic Resistance Ontology (ARO), CARD has updated the ARO to categorize AMEs with the nomenclature guidelines. The revised ontology is reflective of AME biochemistry and phenotype, with strict definitions for each allele family based on antibiotic susceptibility testing. Planning of an interactive web interface to assist authors in naming and analyzing proposed novel AMEs is underway. Going forward, novel published AMEs will only be included in CARD if they have a unique proper name. There remains an ongoing process to review existing AMEs and resolve naming conflicts, for which CARD will engage authors and the research community for feedback.

• Hande McGinty, Hande McGinty, Kansas State University
• Aryan Dalal, Aryan Dalal, Kansas State University
• Duru Dogan, Duru Dogan, Kansas State University
• Atalay Mert Ileri, Atalay Mert Ileri, Kansas State University
• Yinglun Zhang, Yinglun Zhang, Kansas State University

Flavonoids are polyphenolic compounds found in plants and naturally occur in fruits, vegetables, teas, wines, and chocolate. Flavonoids also have known health benefits due to their anti-oxidative, anti-inflammatory, anti-mutagenic, and anti-carcinogenic properties and their ability to inhibit/modulate enzymatic systems. During this research we explored the relationships among different flavanoids, different foods, and different cancers using knowledge graphs and statistical methods. Our preliminary results show that the relationships among these concepts are more complex than the insights simple statistical methods can provide. In this presentation, we present our approach using KNARM methodology to data collection, data cleaning, and representation using graph databases and knowledge graphs in addition to the preliminary results of our statistical approaches. As we continue our research, we're enriching our knowledge graph by incorporating data on known cancer drugs and drug targets to the knowledge graph and adopting more complex analysis approaches to understand the dynamic interplay of flavanoid-food-cancer interactions as well as using Large Language Models (LLMs) for enhancing our knowledge graph.

• Tiffany Callahan
• Robert Hoehndorf

Wrap-up and open time for questions