Heeju Noh

Model-based Standardization of Correlation Coefficients Improves Multi-Omic Clustering and Biological Signal Discovery

The paper introduces a method to standardize correlation coefficients across batches in multi-omic data, improving clustering and biological signal discovery by removing batch-specific biases.

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.

Identification and Targeting of Regulators of SARS-CoV-2-Host Interactions in the Airway Epithelium.

The paper identifies master regulator modules in human airway epithelial cells reprogrammed by SARS-CoV-2 and finds 11 drugs that reverse these signatures.

pyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

pyVIPER is a Python package for fast, scalable rank-based enrichment analysis of scRNA-seq data, offering significant runtime improvements over VIPER for large datasets.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.

Interrogation of genome-wide, experimentally dissected gene regulatory networks reveals mechanisms underlying dynamic cellular state control

The paper uses CRISPRi and single-cell RNA-seq to reverse engineer gene regulatory networks in colorectal cancer cells, revealing autoregulatory and cooperative mechanisms.

DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

DeltaNeTS+ infers direct gene targets of drugs and diseases from gene expression data, showing effectiveness in complex organisms and time-course profiles.

Network Insights into Improving Drug Target Inference Algorithms

The paper evaluates how network and gene expression data affect ProTINA's accuracy, finding that network data dominates and introduces TREAP, a new algorithm with higher accuracy.

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection

ProTINA infers protein targets of drugs and influenza A virus by analyzing gene expression changes, revealing accurate targets and infection mechanisms.