Dillon Hammill

Superior batch alignment and hyper-dimensional cytometry representations allow ultra-sensitive classification of disease phenotypes

This paper introduces a neural network-based batch alignment method for cytometry data that preserves biological signals and enables hyperdimensional representation for improved disease phenotype classification.

Benchmark of Wide Range of Pairwise Distance Metrics for Automated Classification of Mouse Mutant Phenotypes from Flow Cytometry Data

The paper benchmarks pairwise distance metrics for classifying mouse mutant phenotypes from flow cytometry data, finding cosine similarity and standardized Euclidean distance perform best.