Allison Creason

GLEAM provides a web-based toolkit for accessible and rigorous machine learning analyses in biomedical research, producing accurate models across three tasks.

The paper identifies resistance mechanisms to KRAS-MEK pathway targeting in PDAC by analyzing clinical trial data and multi-omics profiles, showing that KRAS/MYC co-gain correlates with resistance and that co-targeting these genes is synergistic.

This study examines how the spatial organization of the tumor-immune microenvironment changes during treatment in ER-positive breast cancer and links these changes to clinical response.

COEXIST integrates serial multiplexed tissue images at the single-cell level by combining molecular profiles with spatial information to improve profiling and platform validation.

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

Galaxy-ME is a web-based software hub for analyzing and visualizing multiplex tissue imaging datasets, demonstrating its utility on normal and cancerous tissue data.

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

The paper introduces Galaxy-ML, a web-based platform for reproducible and scalable supervised machine learning in biomedicine, extending the Galaxy computational workbench.