Nathan D. Price

This study identifies that plasma proteins selectively reflect tissue-specific aging processes in muscle and ovary, revealing context-dependent age-related changes.

The paper uses a multiomics approach to identify placental network differences among obstetric syndromes, finding unique signatures associated with specific clinical outcomes.

The paper identifies altered huntingtin-chromatin interactions in HD mouse models, linking them to transcriptional and epigenetic changes in striatal tissue.

The paper identifies APOE genotype-related metabolite associations with biological age and bioenergetics in humans, showing counterintuitive patterns in {varepsilon}2 and {varepsilon}4 carriers.

The paper constructs a genome-scale transcriptional regulatory network of the human placenta using genomic and transcriptomic data, identifying key regulators of preterm birth.

This study identifies blood-based biomarkers associated with Alzheimer's disease genetic risk variants in a large cohort of healthy adults, showing effects from early adulthood and sex-specific patterns.

The paper evaluates 54 polygenic risk scores in long-lived individuals and their offspring, finding lower genetic risk for Alzheimer's, CAD, and lupus, but not for dementia in long-lived individuals.

iNetModels 2.0 provides an interactive platform for visualizing and exploring multi-omics associations in human health and disease.

The paper shows that gut microbiome uniqueness increases with age and predicts survival in humans.

This study examines how polygenic risk scores for cancer and longevity-associated variants relate to cancer risk and finds that longevity scores do not significantly improve cancer risk prediction beyond cancer-specific scores.

The paper develops a workflow to identify transcription factor binding sites across 27 human tissues using DNase-seq data, resulting in a comprehensive footprint atlas with high-quality predictions.

The paper quantifies transcription factor abundances during human erythropoiesis and reveals gene regulatory principles and imbalances in coactivator-corepressor ratios.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

iREAD detects intron retention events from RNA-seq data by analyzing read depth and distribution patterns, producing a list of retained introns for further analysis.

The paper identifies 10 blood proteins with altered serum levels in early Lyme disease patients, developing a 10-protein panel for potential diagnostic use.

The paper identifies differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease by analyzing transcriptomes and metabolomics data from brain samples.

The paper identifies a blood metabolome signature that robustly predicts gut microbiome α-diversity across health and disease conditions.

The paper identifies miRNA expression differences in whole blood and monocytes of women with spontaneous preterm labor compared to controls, linking them to immune pathways.

The paper presents tools for ensuring FAIRness in big biomedical data analysis through a case study on creating a transcription factor binding site atlas from ENCODE data.

The paper constructs a brain-specific transcriptional regulatory network model to identify core transcription factors involved in psychiatric and neurodegenerative diseases.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.

SIGDA provides detailed, robust visualizations of human ancestry and population structure from genetic data, outperforming PCA in detecting population axes and relationships.

The paper introduces a fast hashing method for comparing semi-structured data, demonstrating its use on electronic health records for duplicate detection and clustering.

BDQC identifies data outliers and anomalies in large-scale genomic pipelines without domain knowledge, improving data quality and enabling new insights.

ProbAnnoWeb and ProbAnnoPy fill gaps in metabolic reconstructions by using probabilistic annotations specific to the target organism.

The paper constructs a network of reactions for plant cell wall polysaccharide degradation in Neurospora crassa by integrating multiple data types and identifies a key transcription factor in mannan degradation.

The paper introduces IDREAM, a method that combines gene regulatory and metabolic networks to improve phenotype prediction in yeast, showing better accuracy than existing methods.

This study shows that reducing huntingtin in peripheral organs of HD mice does not improve central nervous system disease signs.

This study shows that B6.HttQ111/+ mice exhibit early transcriptional and proteostatic deficits before synapse loss and neurodegeneration in HD.