Quantitative Image Analysis – Predicting genomic effects in high-throughput microscopy
Intro/motivation Recently, machine-learning techniques, particularly “deep learning” methods, have garnered significant attention for their potential utility in biomedical applications. Previously, predictive models often relied on expert knowledge and models that incorporated some number of known “features”; a simple example is the prediction of cardiovascular disease using a panel of covariates such as smoking history, HDL, … Continue reading “Quantitative Image Analysis – Predicting genomic effects in high-throughput microscopy”
