Robert C. Clarke

Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

The paper develops a stochastic Boolean model of apoptosis signaling in MCF7:5C cells to predict cell death inducers and inhibitors, identifying genetic and drug-target combinations that affect apoptosis pathways.

ABDS: tool suite for analyzing biologically diverse samples

ABDS provides tools for analyzing biologically diverse samples by addressing missing value imputation, signature gene detection, and expression pattern visualization.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

swCAM estimates subtype-specific expressions in individual samples using unsupervised deconvolution, revealing changes in cell proportions and gene expression in bipolar disorder.

Determining molecular archetype composition and expression from bulk tissues with unsupervised deconvolution

This paper introduces an unsupervised deconvolution method, CAM3.0, to determine molecular archetype composition and expression from bulk tissues, showing its effectiveness through simulations and case studies.

DDN2.0: R and Python packages for differential dependency network analysis of biological systems

DDN2.0 provides R and Python tools for differential dependency network analysis to identify key pathways and network rewiring across omics data.

Data-driven detection of subtype-specific and differentially expressed genes

This paper introduces OVE-FC and OVE-sFC tests to detect subtype-specific and differentially expressed genes, showing improved accuracy in deconvolution compared to existing methods.

Inferring active cis-regulatory modules to predict functional regulatory elements

The paper develops ChIP-GSM, a Bayesian method to infer cis-regulatory modules from ChIP-seq data, showing improved performance in predicting regulatory element activity.

BICORN: An R package for integrative inference of de novo cis-regulatory modules

BICORN integrates transcription factor binding and gene expression data to infer sparse cis-regulatory modules and their regulatory networks.

Integrated Identification of Disease Specific Pathways Using Multi-omics data

This paper proposes two computational methods to integrate multi-omics data and identify disease-specific pathways, demonstrating their effectiveness on muscular dystrophy data.

Unsupervised deconvolution of molecular heterogeneity uncovers novel signatures and glia-neuron ratio

The paper investigates the glia-neuron ratio in the brain using unsupervised deconvolution methods on single-cell data from human brains.

Mathematical modelling of molecular heterogeneity identifies novel markers and subpopulations in complex tumors

The paper develops an unsupervised method to identify marker genes and subpopulations in tumors without prior knowledge of marker genes or subpopulation composition.