Paul N. Schofield

PhysioMap: an ontology-grounded causal knowledge graph of human physiology

PhysioMap connects physiological traits to causal relationships and predicts responses in metabolic diseases, showing agreement with references after excluding discordant cases.

PAVS: A Standardized Database of Phenotype-Associated Variants from Saudi Arabian Rare Disease Patients

PAVS is a database of phenotype-associated variants from Saudi Arabian rare disease patients, enabling gene prioritization through phenotype-based similarity search.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

The paper assesses how model organism phenotypes contribute to identifying human disease genes and finds that only mouse phenotypes accurately predict human gene-disease associations.

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

Klarigi: Characteristic Explanations for Semantic Data

Klarigi identifies compositional and discriminatory ontology classes for groups of biomedical entities, providing semantic explanations that extend existing knowledge.

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration

DeepMOCCA integrates multi-omics data with biological networks to predict cancer survival and identify patient-specific prognostic markers.

DDIEM: Drug Database for Inborn Errors of Metabolism

DDIEM compiles therapeutic strategies for 299 inborn errors of metabolism, classifying treatments by mechanism of action using a new ontology.

Ontology-based prediction of cancer driver genes

This paper introduces a method to predict cancer driver genes using ontology-based features and validates it in nasopharyngeal and colorectal cancers.

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

PathoPhenoDB links human pathogens to disease phenotypes to support infectious disease research by integrating pathogen-disease associations and drug resistance information.

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

The paper evaluates ontology design patterns for combining pathology and anatomy ontologies using an aging mice dataset, showing how different patterns perform in analysis tasks.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

DeepPVP prioritizes causative variants using deep learning and phenotype similarity, outperforming existing methods in speed and accuracy.

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

OligoPVP identifies oligogenic disease variants by integrating patient phenotypes and gene interactions in whole exome or genome sequences, showing improved performance over existing methods.

Ontology-based validation and identification of regulatory phenotypes

The paper develops an ontology-based method to validate consistency between function and phenotype annotations and predicts phenotypes from functions, improving annotation quality and enabling new biological insights.