Brock C. Christensen

This study shows that EMT progression in breast cancer correlates with reduced immune cell infiltration and function, including decreased cytotoxic T cells and altered B cell composition.

This study identifies DNA methylation alterations in tumor endothelial cells using bulk specimens and deconvolution methods, revealing potential therapeutic targets in breast cancer.

The paper identifies isoform-level changes in ccRCC using DTU analysis, revealing subgroups with distinct survival outcomes and potential biomarkers.

This paper introduces MCI, a method to estimate absolute counts from proportional data using multivariate normal distributions, and demonstrates its effectiveness on white blood cell counts and single-cell mRNA data.

This study identifies RE methylation subtypes in ccRCC linked to immune phenotypes and survival, validated across multiple cohorts.

This study compares RNA extraction and preservation protocols for miRNA profiling in infant and toddler stool, finding that RNAlater and Zymo DNA/RNA Shield yield similar high-quality RNA with comparable human miRNA profiles.

The paper develops a continuous DNA methylation metric (B-Index) to classify CLL subtypes and identifies epigenetic signals linked to B-cell differentiation and tumor burden.

The paper examines if prenatal exposure to genocide or trauma affects epigenetic aging in second-generation clocks among Rwandan adults, finding significant age acceleration linked to prenatal stress.

The paper examines how prenatal exposure to genocide and maternal rape in Rwanda affects DNA methylation in stress-related genes in early adulthood, with effects modulated by postnatal adversity and mental health.

This paper develops a deep learning algorithm for 3D-HDAM to detect dyssynergia, showing comparable accuracy to traditional and hybrid models but better handling of ambiguous cases.

The paper enhances cell deconvolution of peripheral blood using DNA methylation to provide high-resolution immune profiling by including 12 leukocyte subtypes and validated libraries.

The paper evaluates the effectiveness of various -omics data types in predicting cancer mutation states and finds that RNA sequencing is often the best, but other data types can be equally effective.

The paper introduces MethylCapsNet and MethylSPWNet, deep learning models that organize DNA methylation data into biologically relevant capsules to improve interpretability in classifying CNS tumors.

The paper introduces GCN4R, an R library for fitting graph neural networks on networks to generate embeddings for network-based tasks like community detection.

MethylNet is a deep learning framework for DNA methylation analysis that constructs embeddings, makes predictions, and uncovers heterogeneity with minimal user input.

The paper identifies DNA methylation changes in breast milk associated with future breast cancer risk using epigenome-wide association studies.

The paper evaluates and validates reference and library selection methods for deconvolving cord blood DNA methylation data, finding that the IDOL library outperforms automatic selection methods.

This study applies variational autoencoders to breast tumor DNA methylation data to identify differential methylation patterns and regulatory regions.

The paper maps 5-hydroxymethylcytosine (5hmC) distribution in normal breast tissue and links it to gene regulation and cancer prevention.

The paper develops and validates M2EFM, a methylation-to-expression model, to predict breast cancer outcomes across multiple cohorts with good accuracy and clinical utility.