Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Knowledge graph embeddings in the biomedical domain:Are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks

Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases

Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks

Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases. Our code is available at https://github.com/aryopg/biokge

Modeling gene regulatory networks through data integration

Modeling gene regulatory networks has become a problem of great interest in biology and medical research. Most common methods for learning regulatory dependencies rely on observations in the form of gene expression data. In this dissertation, computational models for gene regulation have been developed based on constrained regression by integrating comprehensive gene expression data for M. tuberculosis with genome-scale ChIP-Seq interaction data. The resulting models confirmed predictive power for expression in independent stress conditions and identified mechanisms driving hypoxic adaptation and lipid metabolism in M. tuberculosis. I then used the regulatory network model for M. tuberculosis to identify factors responding to stress conditions and drug treatments, revealing drug synergies and conditions that potentiate drug treatments. These results can guide and optimize design of drug treatments for this pathogen. I took the next step in this direction, by proposing a new probabilistic framework for learning modular structures in gene regulatory networks from gene expression and protein-DNA interaction data, combining the ideas of module networks and stochastic blockmodels. These models also capture combinatorial interactions between regulators. Comparisons with other network modeling methods that rely solely on expression data, showed the essentiality of integrating ChIP-Seq data in identifying direct regulatory links in M. tuberculosis. Moreover, this work demonstrates the theoretical advantages of integrating ChIP-Seq data for the class of widely-used module network models. The systems approach and statistical modeling presented in this dissertation can also be applied to problems in other organisms. A similar approach was taken to model the regulatory network controlling genes with circadian gene expression in Neurospora crassa, through integrating time-course expression data with ChIP-Seq data. The models explained combinatorial regulations leading to different phase differences in circadian rhythms. The Neurospora crassa network model also works as a tool to manipulate the phases of target genes

Approximately Counting Answers to Conjunctive Queries with Disequalities and Negations

We study the complexity of approximating the number of answers to a small query \varphi in a large database D. We establish an exhaustive classification into tractable and intractable cases if \varphi is a conjunctive query possibly including disequalities and negations: - If there is a constant bound on the arity of \varphi, and if the randomised Exponential Time Hypothesis (rETH) holds, then the problem has a fixed-parameter tractable approximation scheme (FPTRAS) if and only if the treewidth of \varphi is bounded. - If the arity is unbounded and \varphi does not have negations, then the problem has an FPTRAS if and only if the adaptive width of \varphi (a width measure strictly more general than treewidth) is bounded; the lower bound relies on the rETH as well. Additionally we show that our results cannot be strengthened to achieve a fully polynomial randomised approximation scheme (FPRAS): We observe that, unless NP =RP, there is no FPRAS even if the treewidth (and the adaptive width) is 1. However, if there are neither disequalities nor negations, we prove the existence of an FPRAS for queries of bounded fractional hypertreewidth, strictly generalising the recently established FPRAS for conjunctive queries with bounded hypertreewidth due to Arenas, Croquevielle, Jayaram and Riveros (STOC 2021)