Patrick Cahan

The paper identifies Trps1 as a regulator of knee interzone formation through spatial comparisons and perturbation experiments in mice, showing its role in chondrogenic differentiation.

The paper creates a spatial and single-cell transcriptomic atlas of mouse embryonic synovial joints and their explants, identifying key markers and regulatory mechanisms in joint formation.

CMAC estimates absolute transcriptional age of cardiomyocytes using deep learning, achieving high accuracy across multiple datasets and platforms.

This study shows that inhibiting DDX3 in sarcomas induces a Type I interferon response and reduces metastasis when combined with immunostimulant therapy in mouse models.

The paper introduces IMCell, a method for predicting optimized transcription factor sets for cell fate engineering, showing improved performance over existing tools on synthetic and curated data.

VIDEO is a web-based tool that integrates and visualizes chromatin accessibility, TF binding, and motif occurrence in tissue-specific genes to explore gene regulation in Drosophila.

The paper identifies neurotrophic signals, particularly FGF9, required for fracture healing by mapping somatosensory afferent circuitry to bone and analyzing gene expression changes in DRG neurons.

The paper investigates how CrebA regulates secretory capacity by analyzing gene expression and DNA binding in Drosophila embryos using scRNA-seq and identifies SPCGs as major targets.

This study shows DFMO inhibits Ewing sarcoma metastasis by inducing ferroptosis through polyamine depletion.

The paper investigates DNA methylation changes during pancreatic acinar-ductal metaplasia (ADM) and their persistence without Kras mutation, linking epigenetic memory to signaling pathway alterations and cancer cell plasticity.

This study shows that the small molecule inhibitor WAY262611 inhibits osteosarcoma metastasis by activating Wnt signaling and inducing osteoblastic differentiation.

OneSC simulates synthetic cells across developmental trajectories using Boolean networks inferred from scRNA-seq data, accurately recapitulating cell state transitions and predicting TF perturbation outcomes.

The paper introduces stemFinder, a method to predict single-cell differentiation time using cell cycle gene expression heterogeneity, showing it is computationally efficient and performs as well as or better than existing methods.

The paper uses model-X knockoffs to assess false positives in regulatory network inference and identifies causal sufficiency as a major limitation.

This study identifies the Hippo-YAP/TAZ-TEAD axis as a regulator of bone marrow stromal cell multipotency through computational analysis of single-nucleus multiomics data.

This study benchmarks expression forecasting methods using 11 perturbation datasets, finding that simple baselines often outperform complex models using mean squared error.

This paper improves gene regulatory network reconstruction by combining a new algorithm with a k-nearest neighbor mutual information estimator, achieving better precision-recall performance on synthetic datasets.

PACNet enables platform-agnostic comparison of cell fate engineering protocols by analyzing transcriptomic data from multiple studies and identifying best practices and off-target effects.

The paper introduces Epoch, a tool for dynamic GRN reconstruction, and shows it identifies topology changes in mouse ESC differentiation networks driven by signaling pathways.

The paper uses single-cell RNA-Seq to map transcriptional programs in synovial joint development, identifying a progenitor population and the role of FGF signaling in chondrocyte fate.

The paper creates a single cell atlas of biomaterials response using scRNA-seq and develops a tool to analyze intercellular signaling patterns in the context of biomaterials.

The paper compares in vitro and in vivo embryogenesis using scRNA-seq data and computational tools to evaluate stem cell differentiation protocols.

The paper evaluates the transcriptional fidelity of cancer models using a machine learning tool called CancerCellNet, finding that genetically engineered mice and tumoroids generally show higher fidelity than other model types.

The paper constructs a single-cell transcriptional atlas of synovial joint progenitors from E12.5 to E15.5 in mice to uncover transcriptional programs and cell interactions during joint development.

SingleCellNet classifies single cell RNA-Seq data across platforms and species by comparing query data to reference data, showing improved accuracy and cross-species performance.

The paper introduces Radiator, a cloud-based framework for deploying bioinformatics tools as web applications, and describes its implementation and availability.