Author

A. Tannenbaum

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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Cancer Biology· 4 Bioengineering· 1 Biophysics· 1 Physiology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 7 Research Focus:cell type annotation· 4 Organism:human· 4 Assay:bulk RNA-seq· 3 Approach:deep learning· 3 Research Focus:differential expression· 3 Research Focus:clustering· 2 Assay:imaging· 2

Frequent Co-Authors

J. Deasy· 4 shared M. Pouryahya· 4 shared J. Mathews· 3 shared S. Nadeem· 3 shared A. Apte· 2 shared A. Iyer· 2 shared J. O. Deasy· 2 shared A. G. Mikos· 1 shared

Activity by Year

2017-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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4 shared papersCancer Biology

J. Deasy

Mutual co-authors in cluster

Author profile →

7 papers

Recent·Most cited

Mapping the Single-cell Differentiation Landscape of Osteosarcoma

D. D. Truong, C. Weistuch, K. A. Murgas, P. Admane, B. L. King, J. Chauviere Lee, S.-E. Lamhamedi-Cherradi, J. Swaminathan, N. C. Daw, N. Gordon, V. Gopalakrishnan, R. G. Gorlick, N. Somaiah, J. O. Deasy, A. G. Mikos, , J. Ludwig

The paper maps the differentiation landscape of osteosarcoma using single-cell data to identify gene modules linked to cell fate and patient survival.

bioRxiv · Sep 13, 2023Cancer Biology

Optimal Mass Transport for Robust Texture Analysis

Z. Belkhatir, A. Iyer, J. Mathews, M. Pouryahya, S. Nadeem, J. Deasy, A. Apte, A. Tannenbaum

This paper introduces a texture analysis method using optimal mass transport to improve robustness in CT slice classification for head and neck cancer, outperforming random forest on scalar features.

bioRxiv · Nov 27, 2019 · PreprintBioengineering

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

J. H. Oh, A. Apte, E. Katsoulakis, N. Riaz, V. Hatzoglou, Y. Yu, J. Leeman, U. Mahmood, M. Pouryahya, A. Iyer, A. Shukla-Dave, A. Tannenbaum, N. Lee, J. Deasy

This paper develops a network-based method to identify reproducible radiomic features across different imaging settings and validates it using phantom and external datasets.

bioRxiv · Sep 19, 2019 · PreprintBiophysics

Glymphatic Optimal Mass Transport with Lagrangian Workflow Reveals Advective and Diffusion Driven Solute Transport

S. Koundal, R. Elkin, S. Nadeem, Y. Xue, S. Constantinou, S. Sanggaard, X. Liu, B. Monte, F. Xu, W. Van Nostrand, M. Nedergaard, H. Lee, J. Wardlaw, H. Benveniste, A. Tannenbaum

This study uses rOMT to analyze glymphatic transport, showing faster solute speed in CSF and regional differences in brain parenchyma, with implications for neurodegenerative diseases.

bioRxiv · Sep 14, 2019Physiology

Robust and Interpretable PAM50 Reclassification Exhibits Survival Advantage for Myoepithelial and Immune Phenotypes

J. Mathews, S. Nadeem, A. J. Levine, M. Pouryahya, J. O. Deasy, A. Tannenbaum

The paper reclassifies breast tumors into 7 classes using interpretable mRNA signatures, showing improved survival outcomes for myoepithelial and immune-related phenotypes.

bioRxiv · Jul 16, 2019Cancer Biology

Molecular phenotyping using networks, diffusion, and topology: soft tissue sarcoma

J. Mathews, M. Pouryahya, C. Moosmueller, I. Kevrekidis, J. Deasy, A. Tannenbaum

The paper introduces a network-based analysis method to identify gene expression signatures in sarcomas, revealing known and novel subtypes associated with p53 signaling.

bioRxiv · May 24, 2018Cancer Biology

Pediatric Sarcoma Data Forms a Unique Cluster Measured via the Earth Mover’s Distance

Y. Chen, F. Dela Cruz, R. Sandhu, A. Kung, P. Mundi, J. Deasy, A. Tannenbaum

The paper clusters pediatric and adult sarcoma data using optimal transport theory and identifies a unique pediatric cluster.

bioRxiv · Mar 13, 2017Cancer Biology