Peter K. Rogan

This paper describes ADCI Online, a secure cloud-based platform for radiation dose estimation using dicentric chromosome assays, achieving results comparable to local systems in under 2 days.

The paper improves radiation exposure detection in blood by addressing confounding factors through enhanced gene signatures.

ADCI-HT automates dicentric chromosome detection and dose estimation for large-scale radiation incidents, achieving high throughput and accuracy.

This paper improves automated cytogenetic biodosimetry by correcting false positive dicentric chromosomes to estimate partial body radiation exposure.

The paper confirms that genomic variants predicted to affect alternative mRNA splicing indeed alter splice site strength and exon inclusion, using RNAseq and q-RT-PCR in HapMap and tumor datasets.

The paper uses machine learning to predict genes with similar tissue-wide expression patterns based on transcription factor binding site clusters and chromatin accessibility, showing that dense clusters buffer against mutations.

The paper presents a validated pan-cancer repository of mRNA splicing mutations, identifying and characterizing over 350,000 mutations across multiple tumor types.

The paper improves radiation dose estimation by automating dicentric chromosome curation and metaphase cell selection, reducing false positives and increasing accuracy.

The paper presents a centromere detection algorithm for human metaphase chromosomes, achieving 87% accuracy by evaluating candidate locations using profile thickness measurements and correcting for sister chromatid separation.