Author

Samuel A. Danziger

Center for Infectious Disease ResearchORCID 0000-0002-5669-414XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Systems Biology· 4 Bioinformatics· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

John D. AitchisoniD· 3 shared Nitin S. BaligaiD· 3 shared Alexander V. RatushnyiD· 2 shared David J. ReissiD· 2 shared Jennifer J. SmithiD· 2 shared Matthew W. B. TrotteriD· 2 shared Adrián López García de LomanaiD· 1 shared Alexis KaushanskyiD· 1 shared

Activity by Year

2017-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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3 shared papersSystems Biology

John D. AitchisonORCID

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Author profile →

5 papers

Recent·Most cited

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Matthew A. Wall, Serdar Turkarslan, Wei‐Ju Wu, Samuel A. Danziger, David J. Reiss, Mike J. Mason, Andrew Dervan, Matthew W. B. Trotter, Douglas E. Bassett, Robert Mark Hershberg, Adrián López García de Lomana, Alexander V. Ratushny, Nitin S. Baliga

The paper identifies 141 genetic programs in multiple myeloma that predict disease outcomes and reveal mechanisms of relapse and therapy response.

npj Precision Oncology · Jun 28, 2021Peer-reviewedSystems Biology

A system-level gene regulatory network model for Plasmodium falciparum

Maxwell Lewis Neal, Ling Wei, Eliza J. R. Peterson, Mario L. Arrieta‐Ortiz, Samuel A. Danziger, Nitin S. Baliga, Alexis Kaushansky, John D. Aitchison

The paper develops a gene regulatory network model for Plasmodium falciparum using machine learning, showing it predicts gene expression and identifies correlates of artemisinin resistance.

Nucleic Acids Research · Jan 7, 2021Peer-reviewedSystems Biology

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

Samuel A. Danziger, David L. Gibbs, Ilya Shmulevich, Mark T. McConnell, Matthew W. B. Trotter, Frank Schmitz, David J. Reiss, Alexander V. Ratushny

ADAPTS improves cell type deconvolution in single-cell RNAseq data by customizing signature matrices and using hierarchical clustering to refine cell type identification.

PLoS ONE · Nov 19, 2019Peer-reviewedBioinformatics

An indicator cell assay for blood-based diagnostics

Samuel A. Danziger, Leslie Rae Miller, Karanbir Singh, G. Adam Whitney, Elaine R. Peskind, Ge Li, Robert J. Lipshutz, John D. Aitchison, Jennifer J. Smith

The paper introduces an Indicator Cell Assay Platform (iCAP) for blood-based diagnostics, demonstrating its ability to detect pre-symptomatic diseases in animal models and early-stage Alzheimer's in humans with high accuracy.

PLoS ONE · Jun 8, 2017Peer-reviewedSystems Biology

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Zhuo Wang, Samuel A. Danziger, Ben Heavner, Shuyi Ma, Jennifer J. Smith, Li Song, Thurston Herricks, Evangelos Simeonidis, Nitin S. Baliga, John D. Aitchison, Nathan D. Price

The paper introduces IDREAM, a method that combines gene regulatory and metabolic networks to improve phenotype prediction in yeast, showing better accuracy than existing methods.

PLoS Computational Biology · May 17, 2017Peer-reviewedSystems Biology