Kenneth Daily

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

This study identifies somatic SNVs in non-cancerous human brain tissues and establishes best practices for detecting low-frequency variants.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

This paper introduces a cell tracking method based on Voronoi partition and stable matching to improve tracking accuracy from coarsely sampled live-cell imaging data.

The paper develops a multi-view evidence aggregation method to identify and rank potential driver genes of Alzheimer's Disease using RNA-Seq data and GWAS summary statistics.