Tessa Green, PhD, KiraGen's Head of Immunoinformatics, has contributed to three studies published since she joined the company, spanning tumor biology, single-cell analysis, and the reliability of biomedical machine learning.
In Nature Methods, she contributed methods and implementation to pertpy: an end-to-end framework for perturbation analysis, an open-source framework for analyzing cellular responses to experimental perturbations.
The eLife Reviewed Preprint Identifying tissue states by spatial protein patterns related to chemotherapy response in triple-negative breast cancer examined how spatial organization within triple-negative breast tumors relates to chemotherapy response, drawing on more than four million cells from 63 patients.
In Oxford Open Immunology, Tessa served as co-first and corresponding author on Algorithm dependence of patient phenotypes in Long COVID, a study examining how algorithm choice shapes apparent patient subtypes and underscoring the importance of testing whether computational findings remain consistent across methods.
Together, these publications reflect the combination of tumor-microenvironment biology, computational method development, and rigorous evaluation of biological evidence that Tessa brings to KiraGen's research strategy.