Mansooreh Ahmadian

This paper quantifies sex imbalance in biomedical research by analyzing transcriptome samples and study metadata, revealing male-skewed representation in many disease areas.

This paper introduces txt2onto 2.0, a method for annotating biomedical metadata with disease and tissue terms using interpretable machine learning, showing improved performance over its predecessor.

The paper uses spatial data from ovarian cancer tumors to show that spatial organization of the immune microenvironment correlates with patient outcomes and can predict treatment response.

The paper introduces FunSpace, a method using spatial entropy and functional principal component analysis to assess spatial heterogeneity in tumor microenvironments and its impact on survival.

The paper introduces a platform-independent preprocessing framework for multiplex tissue imaging data to improve cross-platform consistency and reliability.

This paper develops a hybrid stochastic model to study noise effects on budding yeast cell cycle control, achieving efficient simulations matching experimental data.