Jeremy L. Muhlich

This study uses 3D reconstruction and optimal transport modeling to analyze TLS development in TB-infected lungs, revealing distinct maturation trajectories and spatial dynamics.

SEAL integrates spatial imaging context with dimensionality reduction embeddings to improve interpretation of complex datasets through interactive visualization and feature importance analysis.

The paper describes how the Human Tumor Atlas Network shares data through standards, infrastructure, and community engagement, making datasets accessible via multiple platforms.

This study uses spatial profiling to show immune suppression and microenvironment changes in fallopian tube precursors of high-grade serous ovarian cancer.

This study identifies an estrogen receptor signaling program in HR+ breast cancer that suppresses immune responses by reducing T cell infiltration and HLA expression, suggesting therapeutic targets to enhance immunogenicity.

The paper identifies a subset of mismatch repair proficient colorectal cancers with high immune infiltration that may benefit from immune checkpoint inhibitors, using spatial profiling and genomics.

psudo is an interactive system that optimizes pseudocolor palettes for multichannel biomedical images to enhance perceptual distinction and reduce color confusion in overlapping channels.

The paper discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

The paper models MAPK signaling in BRAFV600E melanoma cells to show how allostery and rewiring lead to drug resistance.

ASHLAR is a Python tool that stitches and registers highly multiplexed whole slide images to generate accurate mosaics, outperforming existing software.

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.

piNET is a web platform for analyzing and visualizing proteomics data, including PTMs, by connecting them to biological knowledge and LINCS signatures.

The paper introduces INDRA, a tool that converts natural language descriptions of molecular mechanisms into executable computational models of biological networks.