Michael R. Brent

METANet: A supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks

METANet predicts direct, functional TF targets across human tissues by integrating motif, cis-regulatory, and expression features, outperforming existing methods.

CIDER: detecting changes in gene regulatory networks that are associated with changes in phenotype

CIDER detects gene regulatory network changes associated with continuous traits by modeling interactions between transcription factors and target genes, outperforming two-group methods in simulations and identifying trait-dependent regulatory relationships in human transcriptomes.

Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies

This study identifies age and survival-related transcriptomic signatures in longevity-enriched cohorts using RNAseq data from blood samples.

Ubiquitous functional synergy partially explains why most transcription factor binding is non-functional

The paper shows that functional synergy between transcription factors explains why most TF binding does not affect gene expression, and highlights the need for more expressive regulatory network models.

Mapping the transcriptional regulatory network of a fungal pathogen by exploiting transcription factor perturbation

The paper maps the transcriptional regulatory network of Cryptococcus neoformans by analyzing TF deletions and identifies functionally orthologous TF pairs across species.

Robust Serum Proteomic Signatures of APOE2

The paper validates and expands a serum protein signature associated with APOE genotypes, identifying new protein associations and potential targets for Alzheimer's disease prevention.

FISHNET: A Network-based Tool for Analyzing Gene-level P-values to Identify Significant Genes Missed by Standard Methods

FISHNET identifies significant genes missed by standard methods by analyzing gene-level P-values with network and function-based approaches, showing improved replication rates in cardiovascular traits.

Gene-Embedded Multi-Modal Networks for Population-Scale Multi-Omics Discovery

GEM-Net constructs multi-modal gene networks using harmonized omics data from the Long Life Family Study, revealing a microbiome-immune axis linked to metabolic health.

Discovery of genomic and transcriptomic pleiotropy between kidney function and soluble receptor for advanced glycation end-products using correlated meta-analyses: The Long Life Family Study (LLFS)

The paper identifies pleiotropic genetic factors shared between kidney function and sRAGE using GWAS and TWAS in the LLFS cohort.

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS)

The paper identifies ARHGAP44 as a novel gene associated with longitudinal changes in HbA1c in non-diabetic individuals using linkage and sequence analysis from two studies.

The response of Dual-Leucine Zipper Kinase (DLK) to nocodazole: evidence for a homeostatic cytoskeletal repair mechanism

This study shows that DLK signaling is activated by low-dose nocodazole and mitigates microtubule damage, revealing a cytoskeletal homeostatic response pathway in neurons.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

The paper integrates multi-omics data to identify genes influencing cardiovascular risk traits in the Long Life Family Study, finding 64 significant genes and replicating 29 in the Framingham Heart Study.

Calling Cards: a customizable platform to longitudinally record protein-DNA interactions over time in cells and tissues

Calling Cards records protein-DNA interactions over time using transposons, enabling analysis of historical molecular states through sequencing.

Leveraging a new data resource to define the response of C. neoformans to environmental signals

The paper systematically analyzes how environmental signals affect capsule size and gene expression in C. neoformans using a large RNA-Seq dataset.

Predicting which genes will respond to perturbations of a TF: TF-independent properties of genes are major determinants of their responsiveness

The paper predicts gene responses to TF perturbations using TF-independent features, finding that gene expression level and variation are more predictive than TF binding or histone marks.

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

The paper evaluates methods to infer transcription factor (TF) activity from gene expression data using constraints from TF perturbation data, showing constraints are essential for performance.

MAPPING TRANSCRIPTION FACTOR NETWORKS BY COMPARING TF BINDING LOCATIONS TO TF PERTURBATION RESPONSES

The paper improves transcription factor network mapping by combining dual threshold optimization, NetProphet 2.0, and new methods for binding and response data.