Biology subjects

Polychronopoulos, D.

Publications and source records attributed to Polychronopoulos, D..

4 recordsLinked to original sources

bioRxiv2024

Community-aware explanations in knowledge graphs with XP-GNN

Machine learning applications for the drug discovery pipeline have exponentially increased in the last few years. An example of these applications is the biological Knowledge Graph. These graphs represent biological entities and the relations between them based on existing knowledge. Graph machine learning models such as Graph Neural Networks can be applied on top of knowledge graphs to support the development of novel therapeutics. Nevertheless, Graph Neural Networks present an improved performance at the expense of complexity, becoming difficult to explain their decisions. State-of-the-art explanation algorithms for Graph Neural Networks focus on determining the most relevant subgraphs involved in their decision-making while considering graph elements (nodes and edges) as independent entities and ignoring any communities these graphs could present. We explore in this work the idea that graph community structure in biological Knowledge Graphs could provide a better grasp of the decision-making of Graph Neural Networks. For that purpose, we introduce XP-GNN, a novel explanation technique for Graph Neural Networks in Knowledge Graphs. XP-GNN exploits the communities of nodes or edges in graphs to refine their explanations, inspired by cooperative game theory. We characterize XP-GNN in a basic example and in terms of scalability and stability. In two relevant use cases for the drug discovery pipeline, XP-GNN provides more relevant explanations than previous techniques, being evaluated quantitatively and by domain experts. At the same time, XP-GNN presents limitations on scalability and stability, which we will address. ACM Reference FormatAndres Martinez Mora, Dimitris Polychronopoulos, Michael Ughetto, and Sebastian Nilsson. 2024. Community-aware explanations in knowledge graphs with XP-GNN. In Proceedings of ACM Conference (Conference17). ACM, New York, NY, USA, 21 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn This work has been funded by AstraZeneca AB, Molndal, Sweden and AstraZeneca Cambridge. Unfortunately, due to proprietary reasons from AstraZeneca AB, the data used in this work cannot be shared.

bioinformatics↗

bioRxiv2021

Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify high value hits resulting from the CRISPR screen involves significant manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we built a hybrid recommendation system on top of a heterogeneous biomedical knowledge graph integrating preclinical, clinical, and literature evidence. Genes were ranked based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identified 36 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identified novel resistance mechanisms that we prospectively validated.

cancer biology↗

bioRxiv2020

Whole genome sequencing for diagnosis of neurological repeat expansion disorders

BackgroundRepeat expansion (RE) disorders affect ~1 in 3000 individuals and are clinically heterogeneous diseases caused by expansions of short tandem DNA repeats. Genetic testing is often locus-specific, resulting in under diagnosis of atypical clinical presentations, especially in paediatric patients without a prior positive family history. Whole genome sequencing (WGS) is emerging as a first-line test for rare genetic disorders, but until recently REs were thought to be undetectable by this approach. MethodsWGS pipelines for RE disorder detection were deployed by the 100,000 Genomes Project and Illumina Clinical Services Laboratory. Performance was retrospectively assessed across the 13 most common neurological RE loci using 793 samples with prior orthogonal testing (182 with expanded alleles and 611 with alleles within normal size) and prospectively interrogated in 13,331 patients with suspected genetic neurological disorders. FindingsWGS RE detection showed minimum 97{middle dot}3% sensitivity and 99{middle dot}6% specificity across all 13 disease-associated loci. Applying the pipeline to patients from the 100,000 Genomes Project identified pathogenic repeat expansions which were confirmed in 69 patients, including seven paediatric patients with no reported family history of RE disorders, with a 0.09% false positive rate. InterpretationWe show here for the first time that WGS enables the detection of causative repeat expansions with high sensitivity and specificity, and that it can be used to resolve previously undiagnosed neurological disorders. This includes children with no prior suspicion of a RE disorder. These findings are leading to diagnostic implementation of this analytical pipeline in the NHS Genomic Medicine Centres in England. FundingMedical Research Council, Department of Health and Social Care, National Health Service England, National Institute for Health Research, Illumina Inc

genomics↗

bioRxiv2019

CNEr: a toolkit for exploring extreme noncoding conservation

Conserved Noncoding Elements (CNEs) are elements exhibiting extreme noncoding conservation in Metazoan genomes. They cluster around developmental genes and act as long-range enhancers, yet nothing that we know about their function explains the observed conservation levels. Clusters of CNEs coincide with topologically associating domains (TADs), indicating ancient origins and stability of TAD locations. This has suggested further hypotheses about the still elusive origin of CNEs, and has provided a comparative genomics-based method of estimating the position of TADs around developmentally regulated genes in genomes where chromatin conformation capture data is missing. To enable researchers in gene regulation and chromatin biology to start deciphering this phenomenon, we developed CNEr, a R/Bioconductor toolkit for large-scale identification of CNEs and for studying their genomic properties. We apply CNEr to two novel genome comparisons - fruit fly vs tsetse fly, and two sea urchin genomes - and report novel insights gained from their analysis. We also show how to reveal interesting characteristics of CNEs by coupling CNEr with existing Bioconductor packages. CNEr is available at Bioconductor (https://bioconductor.org/packages/CNEr/) and maintained at github (https://github.com/ge11232002/CNEr).

genomics↗