Zachary L. Maas

The paper develops ML models for segmenting and classifying cyanobacterial cells, showing improved performance over traditional methods in dense colonies and artifact resistance.

The paper identifies over 800,000 bidirectional transcription regions in human samples and links them to genes through correlated transcription, enriched for disease SNPs and supported by 3D data.

This paper develops a method for supervised deconvolution of nascent sequencing data and finds enhancer RNAs are informative, while undifferentiated cell types complicate deconvolution.

TFEA quantifies transcription factor activity changes from perturbation data, identifying key regulators and temporal regulatory networks.