Author

Eugenia Lyashenko

Columbia UniversityORCID 0000-0002-6538-9462Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1

Frequent Co-Authors

Dennis VitkupiD· 2 shared Mario NiepeliD· 2 shared Purushottam D. DixitiD· 2 shared Baohong ZhangiD· 1 shared Christian MuelleriD· 1 shared Edith L. PfisteriD· 1 shared Fergal CaseyiD· 1 shared Jason WuiD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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2 shared papersSystems Biology

Dennis VitkupORCID

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4 papers

Recent·Most cited

Prediction of Adeno-Associated Virus Fitness with a Protein Language Based Machine Learning Model

Jason Wu, Yu Qiu, Eugenia Lyashenko, Tess Torregrosa, Edith L. Pfister, Michael J. Ryan, Christian Mueller, Sourav Choudhury

The paper develops a machine learning model to predict AAV capsid fitness from amino acid sequences, achieving high accuracy and generalizability across datasets.

Human Gene Therapy · Apr 17, 2025Peer-reviewedBioinformatics

CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

Zhengyu Ouyang, Nathanael Bourgeois, Eugenia Lyashenko, Paige Cundiff, Patrick F. Cullen, Ravi Challa, Kejie Li, Xinmin Zhang, Fergal Casey, Sandi Engle, Baohong Zhang, Maria I. Zavodszky

CellMap infers cell type profiles in bulk RNA-seq data from iPSC-derived cell lines using reference single-cell datasets.

bioRxiv · May 24, 2021 · PreprintBioinformatics

Receptor-Based Mechanism of Cell Memory and Relative Sensing in Mammalian Signaling Networks

Eugenia Lyashenko, Mario Niepel, Purushottam D. Dixit, Sang Kyun Lim, Peter Karl Sorger, Dennis Vitkup

The paper develops and validates a computational model showing mammalian cells use receptor abundance to encode past stimuli for relative sensing in signaling networks.

eLife · Jan 21, 2020Peer-reviewedSystems Biology

Maximum entropy framework for inference of cell population heterogeneity in signaling networks

Purushottam D. Dixit, Eugenia Lyashenko, Mario Niepel, Dennis Vitkup

The paper introduces MERIDIAN, a Maximum Entropy framework to infer signaling network heterogeneity from cell-to-cell variability in network species abundances, and applies it to EGF-induced Akt signaling.

bioRxiv · Apr 22, 2019 · PreprintSystems Biology