Daniel P. Miller

The paper uses a convolutional neural network to predict chromatin organization disruptions caused by somatic structural variants in pediatric tumors, identifying high-impact SVs and regions associated with cancer.

The paper analyzes splicing in 729 pediatric brain tumors, identifies splicing-defined subgroups, and highlights CLK1 exon 4 as an oncofetal event linked to tumor prognosis.

The paper introduces OpenPedCan, a harmonized multi-omic dataset of pediatric cancer patients, providing integrated genomic, transcriptomic, and proteomic data for research and clinical applications.

This study identifies DLK1 as a potential immunotherapeutic target in neuroblastoma through integrative proteomic, transcriptomic, and epigenomic analysis.

AutoGVP classifies germline variants using ClinVar and modified InterVar criteria, enabling large-scale variant pathogenicity assessment in research.

OpenPBTA provides a collaborative platform to genomically characterize 1,074 pediatric brain tumors and 22 cell lines, offering analysis modules and somatic datasets for research and clinical applications.

The paper describes the pipelines and workflows used to process and harmonize genomic data in the NCI Genomic Data Commons, including data types like somatic mutations and gene expression.