Chandra L. Theesfeld

The paper generates disease-specific genetic effect maps on the blood transcriptome for five glomerular diseases using whole genome and transcriptome sequencing data from the CureGN cohort.

Volaria integrates coding and regulatory variation into gene-centered representations for disease outcome prediction using whole-genome sequencing data.

Otari predicts tissue-specific isoform abundance changes caused by genetic variants using a graph-based attention model trained on genomic and long-read transcriptome data.

This study uses whole-exome sequencing and single-cell DNA sequencing to analyze subclonal evolution in breast cancer during aromatase inhibitor treatment, identifying resistance mechanisms and actionable targets.

Mixture modeling of 239 phenotype features in 5,392 SPARK autistic children defines four replicable classes carrying distinct common and rare variant signatures and developmental gene-timing patterns.

DeepArk uses deep learning to model cis-regulatory codes in four model species, accurately predicting regulatory features and variant impacts.

This paper introduces AMBER, an automated framework for designing deep CNNs in genomics, showing it outperforms non-NAS and expert-designed models in accuracy.

The paper identifies RBP target site dysregulation as a major contributor to psychiatric disorder risk, revealing new insights into disease heritability and providing a resource for further research.

The paper uses deep learning to analyze whole-genome data from ASD families, showing noncoding mutations significantly impact transcriptional and post-transcriptional regulation and contribute to autism.