Author

Caroline Moosmüller

Johns Hopkins UniversityORCID 0000-0002-7728-0261Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Approach:optimal transport· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Allen TannenbaumiD· 3 shared Joseph O. DeasyiD· 3 shared Zehor BelkhatiriD· 3 shared Christopher John TralieiD· 2 shared José Santos ReyesiD· 2 shared Maryam PouryahyaiD· 2 shared Anh Phong TraniD· 1 shared Arnold J. LevineiD· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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3 shared papersBioinformatics

Allen TannenbaumORCID

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3 papers

Recent·Most cited

Long-term p21 and p53 trends regulate the frequency of mitosis events and cell cycle arrest

Anh Phong Tran, Christopher John Tralie, Caroline Moosmüller, Zehor Belkhatir, José Santos Reyes, Arnold J. Levine, Joseph O. Deasy, Allen Tannenbaum

The paper analyzes two single-cell datasets to study how p21 and p53 dynamics influence cell cycle arrest and mitosis frequency after radiation exposure.

bioRxiv · Aug 17, 2021 · PreprintSystems Biology

Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

Caroline Moosmüller, Christopher John Tralie, Mahdi Kooshkbaghi, Zehor Belkhatir, Maryam Pouryahya, José Santos Reyes, Joseph O. Deasy, Allen Tannenbaum, Ioannis G. Kevrekidis

The paper introduces a periodicity scoring algorithm for p53 time series and shows its correlation with cell division events.

IFAC-PapersOnLine · Jan 1, 2021Peer-reviewedBioinformatics

Network-based clustering for drug sensitivity prediction in cancer cell lines

Maryam Pouryahya, Jung Hun Oh, James C. Mathews, Zehor Belkhatir, Caroline Moosmüller, Joseph O. Deasy, Allen Tannenbaum

This study introduces a network-based method combining optimal mass transport and machine learning to predict drug sensitivity in cancer cell lines by clustering genomic and chemical features.

bioRxiv · Sep 18, 2019 · PreprintBioinformatics