Katherine Hoff Shutta

This paper shows that LOESS and DE-SWAN can create false 'waves' of molecular aging in data, challenging their statistical validity.

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

The paper identifies DNA-methylation markers at birth and childhood associated with lung function and reveals sex- and genetics-related pathways linked to asthma and lung development.

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

SpiderLearner combines multiple Gaussian graphical model methods to create an ensemble estimate, improving performance and identifying a seven-gene risk score for ovarian cancer.

SpaCeNet identifies intracellular and intercellular molecular networks from spatial omics data by analyzing conditional independence relations between variables within and across cells.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.