Nathan T. Johnson

Machine Learning Identifies Novel Candidates for DrugRepurposing in Alzheimer's Disease

The paper presents a machine learning framework to identify FDA-approved drugs for repurposing in Alzheimer's Disease by analyzing gene lists associated with disease severity and molecular mechanisms.

A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

This paper introduces a hybrid deep learning method for cell type clustering in scRNA-seq data, achieving performance comparable to supervised learning.

The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes

The paper assembles and annotates the genome of the soybean cyst nematode, revealing duplications and horizontal gene transfer events in effector genes involved in parasitism.

Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier?

This study evaluates if isoform-level RNA-Seq data improves supervised machine learning performance in biological classification tasks compared to gene-level data.

Determining rewiring effects of alternatively spliced isoforms on protein-protein interactions using a computational approach

The paper develops a computational tool to predict the effects of alternative splicing on protein-protein interactions and demonstrates its utility in disease-related transcriptomic data.