Tara Eicher

PRISM estimates sample-specific correlation networks for cross-sectional and longitudinal omics data, improving accuracy and uncertainty quantification over existing methods.

BLOBFISH identifies gene regulatory subnetworks connecting predefined gene sets using breadth-first search on bipartite graphs, applied to GTEx data to explore tissue-specific relationships.

FERRET evaluates the robustness of single-cell gene regulatory network inference methods using biological validation and two metrics: RAUC and Monotonicity.

The paper analyzes genomic, transcriptomic, and regulomic data to assess if 'profound autism' is a distinct biological category, finding no evidence for such a category.

SEAHORSE systematically explores associations across heterogeneous omics data from GTEx and TCGA, revealing unexpected links between height and cancer-related pathways, and age with WNT signaling.

This paper introduces GENN, a method for multi-omic interactome analysis that improves prediction of biomedical outcomes by learning salient associations in a low-dimensional space.

RaMP-DB 2.0 provides an updated database and tools for functional interpretation of metabolomic and multi-omic data, with enhanced annotations and enrichment analysis capabilities.