Research Directions

My research focuses on the representation, integration, and analysis of biological knowledge and data. This work is situated at the interface of Artificial Intelligence and bioinformatics, with a primary focus on using formal ontologies to improve scientific discovery.

Foundations

Neuro-symbolic methods in Bioinformatics

I work on methods that integrate symbolic knowledge with statistical learning. This includes mapping entities in formal ontologies into vector spaces while preserving their semantic relations. I develop embedding frameworks for Description Logics (e.g., EL++ and ALC) that provide mathematical guarantees for logical soundness and approximate the interpretation of formalized theories.

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Applied Ontology

I use ontologies to standardize and analyze complex phenotypes across domains. My earlier work focused on foundational ontologies, including the General Formal Ontology (GFO) and its biological extension GFO-Bio, as well as the development of a formal ontology of functions to curate functional knowledge in the life sciences.

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Applications

Protein function prediction

Large-scale ontologies like the Gene Ontology (GO) provide essential background knowledge for understanding protein activity. I work on the DeepGO family of systems, which utilize formalized axioms to constrain deep learning models for protein function prediction. These systems are used to derive functional insights from sequence and interaction data.

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Rare disease diagnostic support

The diagnosis of rare diseases requires the integration of patient-specific data with large-scale background knowledge, such as the Human Phenotype Ontology (HPO). I develop systems like PhenomeNET and PVP that use automated reasoning and machine learning to prioritize disease-causing genomic variants based on their phenotypic consequences.

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Drug mechanisms and systems biology

I apply ontologies and knowledge graphs to model drug-target interactions, drug indications, and adverse drug reactions. This work links molecular data to systems biology through causal knowledge graphs, enabling the identification of mechanistic relationships and potential drug repurposing targets.

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Resources in genomics

I contribute to the development of genomic resources and the analysis of population-specific genomic data. This includes the development of reference genomes, pangenome graphs, and the analysis of antimicrobial resistance.

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