Casey S Greene

Integrative networks illuminate biological factors underlying gene-disease associations

This review discusses integrative networks that combine biological data to model gene interactions and their application in studying gene-disease associations.

Reproducible Computational Workflows with Continuous Analysis

This paper introduces a method for reproducible computational workflows using Docker and continuous integration to automate analysis and ensure reproducibility.

Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification

This paper develops a semi-supervised learning method using denoising autoencoders to extract phenotypes from EHRs, improving classification and survival prediction in ALS data.

Comprehensive cross-population analysis of high-grade serous ovarian cancer supports no more than three subtypes

This study analyzes HGSC subtypes across populations and finds that three subtypes are consistently identified, while a fourth shows less robust cross-population similarity.

A Novel Multi-network Approach Reveals Tissue-specific Cellular Modulators of Fibrosis in Systemic Sclerosis, Pulmonary Fibrosis and Pulmonary Arterial Hypertension

The paper identifies a common immune-fibrotic gene expression signature across SSc, PF, and PAH and uses it to uncover tissue-specific cellular modulators of fibrosis.

Pathway and network-based strategies to translate genetic discoveries into effective therapies

This paper reviews pathway and network-based strategies for drug repositioning from genetic associations, highlighting challenges and opportunities.

ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions

ADAGE, an unsupervised machine-learning method, analyzes Pseudomonas aeruginosa gene expression data to identify gene function, strain differences, and cellular responses.