Genevera I. Allen

The paper shows maternal experience reshapes brain-wide neural responses to infant cries, enhancing efficiency and specialization through oxytocin-dependent circuits.

The paper introduces an ontology-aware classification framework for DNA methylation data using a curated atlas of healthy human tissues and cell types, identifying CpG sites that accurately classify tissues and cell types.

SPA-STOCSY is an automated tool for identifying annotated and non-annotated metabolites in NMR spectra, showing high accuracy and efficiency in both synthetic and real data.

The paper identifies genetic modifiers of alpha-synuclein-mediated neurodegeneration by screening fly homologs of lysosomal storage disorder genes.

The paper introduces a CRISPR toolkit for generating intersectional genetic mice to study cellular circuits, demonstrating its use in neuronal and ovarian populations.

This paper develops a CVP signal processing pipeline to detect junctional ectopic tachycardia, showing comparable performance to ECG-based methods in a pediatric case study.

The paper shows that reducing glial genes involved in synaptic function reduces disease pathology and behavioral issues in a Drosophila Huntington's Disease model.

This paper introduces SCENA, a method that imputes gene correlations in scRNA-seq data using ensemble learning and auxiliary information, improving downstream analyses.

The paper shows strong relationships between brain structural connectomes and human traits using advanced processing and dimensionality reduction techniques on large datasets.

XMRF fits Markov Networks to high-throughput genetics data using Poisson, Ising, and Gaussian graphical models for RNA-seq, mutation, and methylation data.

The paper examines the reproducibility of miRNA profiles in ovarian cancer using TCGA data, finding poor correlation between microarray and miRNA-Seq results, with only one miRNA consistently associated with survival.