Sarah U. Morton

This study shows that RBFOX2 autoregulation is critical for cardiomyocyte differentiation by maturing the transcriptome, and its disruption leads to congenital heart disease.

This paper introduces a deep learning model to detect sulcal pattern deviations in neurological disorders using a large cohort of typically developing individuals.

The paper develops fluorescent probes to detect Paenibacillus thiaminolyticus in mixed populations by removing an S-layer that blocks probe access.

The paper develops a machine-learning model to predict how structural variants disrupt 3D chromatin structure in congenital heart disease and validates its predictions using induced pluripotent stem cells.

This study uses AI to show that genome sequencing can predict adverse outcomes after congenital heart defect surgery by identifying damaging genotypes in specific genes.

This paper proposes a fetal actionable findings list for genomic sequencing in pregnancy to identify clinically relevant incidental variants.

The paper develops a framework for de novo variant identification using FreeBayes and a neural network, achieving high accuracy in SNVs and indels through validation with Sanger sequencing.

The paper identifies mosaic mutations in blood and cardiac tissue from CHD patients, finding 1% of cases have detectable mosaic variants that may contribute to congenital heart disease.

The paper estimates the risk of five disorders from paternal-age-related de novo mutations using whole-exome-sequencing data and Danish registry data.