Theodore J. Perkins

Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry

The paper introduces iDMS, a neural network that predicts optimal separation and compensation voltages for DMS, improving glycosphingolipid analysis.

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

The paper introduces iDMS, a deep neural network that predicts optimal ion mobility parameters for resolving monoglycosphingolipid stereoisomers, improving the efficiency of DMS-based assays.

A multiscale chromatin collapse underlies vascular pathology in Hutchinson -Gilford progeria syndrome

This study identifies chromatin reorganization and histone mark changes in HGPS VSMCs, linking them to vascular pathology through disrupted enhancer-promoter interactions.

Atomic elementary flux modes explain the steady state flow of metabolites in large-scale flux networks

The paper introduces atomic elementary flux modes (AEFMs) to efficiently analyze metabolic fluxes in large networks, showing they outperform traditional EFMs and reveal new insights into carbon and nitrogen metabolism in HepG2 cells.

Histone Modification Metapeaks are Epigenetic Landmarks Predictive of Cell State

The paper identifies histone modification metapeaks across diverse cell and tissue types and shows they can predict cell state using machine learning.

Metabolic, epigenetic and transcriptomic alterations in postnatal 16p11.2 deficient murine astrocytes

This study examines metabolic, epigenetic, and transcriptomic changes in astrocytes from a mouse model of 16p11.2 deletion ASD syndrome, revealing sex-specific molecular alterations.

VNC-Dist: A machine learning-based semi-automated pipeline for quantification of neuronal positioning in the C. elegans ventral nerve cord

VNC-Dist quantifies neuronal positioning in C. elegans VNC using machine learning, improving efficiency and accuracy over manual methods.

Notch-dependent left-right asymmetry in β-catenin-TCF activity instructs anteroposterior positioning of motor neurons in the C. elegans ventral nerve cord

Differential Equation Modeling of Cell Population Dynamics in Skeletal Muscle Regeneration from Single-Cell Transcriptomic Data

The paper develops a non-linear ODE model of skeletal muscle regeneration using scRNA-seq data to capture cell population dynamics and regulatory interactions.

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks

The paper introduces a Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks, demonstrating its application in analyzing lipid metabolism in Alzheimer's disease.

Transcriptional Reprogramming of Skeletal Muscle Stem Cells by the Niche Environment

This study shows that the niche environment significantly influences skeletal muscle stem cell transcriptional activity, with some age-related changes being reversible through exposure to a young niche.

Cell Type Specific DNA Signatures of Transcription Factor Binding

The paper introduces SigTFB, a deep learning method to detect cell type specificity in TF DNA-binding signatures, finding 30% of TFs show such specificity.

KDR Signaling in Muscle Stem Cells Promotes Asymmetric Division and Progenitor Generation for Efficient Regeneration

This study identifies KDR signaling as essential for muscle stem cell asymmetric division and progenitor generation, crucial for efficient muscle regeneration.

BATL: Bayesian annotations for targeted lipidomics

BATL is a Bayesian classifier for targeted lipidomics that annotates LC-ESI-MS/MS peaks with high accuracy using eight features, achieving over 95% correct identification in SRM datasets.

WACS: Improving Peak Calling by Optimally Weighting Controls

WACS improves ChIP-seq peak calling by optimally weighting controls to better model noise distribution, enhancing motif enrichment and reproducibility compared to existing methods.

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data

This study evaluates somatic mutation calling accuracy and reproducibility in clinical-type targeted sequencing data using cell line samples and multiple variant callers.

Myf6/MRF4 is a Myogenic Niche Regulator Required for the Maintenance of the Muscle Stem Cell Pool

This study shows that Myf6/MRF4 regulates myokine production and maintains the muscle stem cell pool by controlling signaling pathways like EGFR and STAT3.

Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis

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

Bayesian Correlation is a robust similarity measure for single cell RNA-seq data

The paper introduces Bayesian correlation as a robust similarity measure for scRNA-seq data, showing it outperforms Pearson correlation in reproducibility and biological relevance.

Human Gene Expression Variability and its Dependence on Methylation and Aging

The paper classifies human genes by expression variability in breast and brain tissues, finding that high variability is unimodal and influenced by age and methylation.

High Resolution Genome Wide Expression Analysis of Single Myofibers Using SMART-Seq

This paper develops smfRNA-Seq to analyze the whole transcriptome of individual myofibers and finds aging causes significant changes in metabolic and structural gene expression.

RECAP reveals the true statistical significance of ChIP-seq peak calls

RECAP corrects statistical biases in ChIP-seq peak calling by recalibrating p-values to provide more accurate false discovery rate estimates.

Uncovering Robust Patterns of MicroRNA Co-Expression across Cancers using Bayesian Relevance Networks

This paper introduces Bayesian Relevance Networks to improve co-expression analysis of miRNA data by accounting for uncertainty in high-throughput sequencing, showing better reproducibility and lower false discovery rates.

COGEM: A Toolbox for Computational Genomics in Matlab

COGEM provides a Matlab toolbox for genomic interval manipulation and analysis, demonstrating its capability in ChIP-seq peak-calling.