Benjamin S. Mashford

The paper introduces GPCT, a pretrained transformer model for cytometry that improves sample-level prediction and interpretability across diverse datasets.

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.

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

This paper demonstrates that linked machine learning classifiers improve species classification of fungi using error-prone long-reads on extended metabarcodes, achieving high recall and low false positives.