James P. Brody

A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.

This paper shows that a compact genotype representation using chromosome-scale length variation can effectively predict phenotypes like sex and height using machine learning.

Genetic risk score for ovarian cancer based on chromosomal-scale length variation.

This paper develops a genetic risk score for ovarian cancer using chromosomal-scale length variation, showing it can stratify risk with high accuracy.

Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.

The paper evaluates a genetic risk score based on chromosome-scale length variation and machine learning to predict severe COVID-19 outcomes, finding limited clinical utility.

Chromosomal scale length variation of germline DNA can predict individual cancer risk

This paper shows that chromosomal-scale length variation in germline DNA can predict cancer risk better than SNP-based methods in two datasets.