Kayla A Johnson

Large-scale automated detection reveals pervasive sex imbalance in biomedical research

This paper quantifies sex imbalance in biomedical research by analyzing transcriptome samples and study metadata, revealing male-skewed representation in many disease areas.

Generative genomics accurately predicts future experimental results

This paper introduces GEM-1, a generative model that accurately predicts future gene expression experiments, achieving performance comparable to real lab experiments.

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata

This paper introduces txt2onto 2.0, a method for annotating biomedical metadata with disease and tissue terms using interpretable machine learning, showing improved performance over its predecessor.

Joint representation of molecular networks from multiple species improves gene classification

GenePlexusZoo improves gene classification by integrating molecular networks from multiple species into a shared feature space, enhancing both single-species and cross-species predictions.

CONE: COntext-specific Network Embedding via Contextualized Graph Attention

CONE generates context-specific gene embeddings from context-free interactomes to identify disease-associated genes using contextualized random walks and graph attention networks.

Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning

The paper uses machine learning on public transcriptome data to predict age and sex from gene expression and identify molecular signatures associated with age and sex differences.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

This study benchmarks 30 RNA-seq data processing workflows for constructing coexpression networks, finding that normalization methods significantly affect network accuracy.

Supervised-learning is an accurate method for network-based gene classification

This study benchmarks supervised-learning for network-based gene classification, showing it outperforms label-propagation and node-embeddings in accuracy.