Author

Leila Azinfar

Vanderbilt University Medical Center

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:statistical model· 3 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Amir AsiaeeiD· 3 shared James Patrick LongiD· 3 shared Heather H. PuaiD· 2 shared Jared StrauchiD· 2 shared Kevin R. CoombesiD· 2 shared Ehsan IrajizadiD· 1 shared Han LiangiD· 1 shared Hongxu ZhuiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersBioinformatics

Amir AsiaeeORCID

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3 papers

Recent·Most cited

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers

Jared Strauch, Leila Azinfar, Heather H. Pua, James P. Long, Kevin R. Coombes, Amir Asiaee

The paper introduces DSEN, a tissue-aware regression method that improves drug response prediction by decomposing models into shared and lineage-specific components, achieving better performance and interpretability.

bioRxiv · Jun 25, 2026 · PreprintBioinformatics

Widespread data leakage inflates accuracy and corrupts biomarker discovery in cancer drug response prediction

Amir Asiaee, Jared Strauch, Leila Azinfar, Samhita Pal, Heather H. Pua, James P. Long, Kevin R. Coombes

The paper shows that data leakage in drug response prediction models inflates accuracy and corrupts biomarker discovery, and provides a solution to avoid this issue.

bioRxiv · Apr 5, 2026 · PreprintBioinformatics

AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models

Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James Patrick Long

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

bioRxiv · Mar 6, 2025 · PreprintBioinformatics