T. Ian Simpson

BatchVaria evaluates batch correction methods by comparing variance changes across configurations using TCGA breast cancer RNA-seq data.

The paper quantifies metadata richness in three biological repositories using NLP metrics, finding differences in word count, information density, and entity consistency across repositories.

This paper uses NLP to analyze 500,000 animal drug studies and links them to clinical data, finding that only 3.1% reach regulatory approval and that species diversity and blinding improve translation success.

cadmus retrieves and processes full-text biomedical literature, achieving high retrieval rates and demonstrating improved coverage of biomedical concepts compared to abstracts.

The paper develops an automated pipeline to map genetic developmental disorder literature to Gene2Phenotype using NLP models, achieving high accuracy and scalability for biocuration.

MOGDx integrates multi-omic data using a graph convolutional network to classify heterogeneous diseases, achieving state-of-the-art performance and identifying relevant genomic markers.

BioNAR is a Bioconductor package for analyzing biological networks to quantify and rank vertices based on topology and clustering, enabling discovery of disease and function-enriched subcomplexes.

This study compares rule-based and ODE models of the DARPP-32 signalling network, finding that rule-based models offer greater flexibility and detail in capturing molecular dynamics.

The paper develops automated methods to extract phenotypic features from literature to improve disease modeling for developmental disorders, showing increased accuracy over manual curation.

The paper uses classification models to identify novel candidate SFARI genes associated with ASD by combining RNA-seq data and gene status in the SFARI-gene list.

The paper constructs a configurable synaptic proteome network model to analyze molecular mechanisms of disease co-morbidity by integrating proteomic and interaction data.

The paper maps proteins in biophysical models of synaptic plasticity, finding 4.2% of synaptic proteins are modelled and identifying gaps in disease-related proteins.