Author

Diane Wuest

Observed names: D. Wuest, D.M. Wuest, Diane M Wuest, Diane M. Wuest, Wuest, Diane Marie

Observed institutions: GNS Healthcare (United States), Delaware Biotechnology Institute, University of Delaware

OpenAlex ID: A5006172830

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation

2017 · Jeanne C Latourelle, Michael T Beste, et al. · The Lancet Neurology · 157

Membrane configuration optimization for a murine in vitro blood–brain barrier model

2012 · Diane M. Wuest, Allison M. Wing, et al. · Journal of Neuroscience Methods · 85

Genomics in mammalian cell culture bioprocessing

2011 · Diane M. Wuest, Sarah W. Harcum, et al. · Biotechnology Advances · 55

Optimization of endothelial cell growth in a murine in vitro blood–brain barrier model

2011 · Diane M. Wuest, Kelvin H. Lee · Biotechnology Journal · 37

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A Bayesian mathematical model of motor and cognitive outcomes in Parkinson’s disease

2017 · Boris Hayete, Diane Wuest, et al. · PLoS ONE · 20

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Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response

2016 · Fred Gruber, Jonathan J Keats, et al. · Blood · 10

Metabolic Engineering

2011 · D.M. Wuest, S. Hou, et al. · Elsevier eBooks · 9

Designing Trials of Disease Modifying Agents for Early and Preclinical Alzheimer's Disease Intervention: What Evidence is Meaningful to Patients, Providers, and Payers?

2019 · Donna A. Messner, P. Rabins, et al. · The Journal of Prevention of Alzheimer s Disease · 4

Amyloid‐β concentration and structure influences the transport and immunomodulatory effects of IVIG

2014 · Diane M. Wuest, Kelvin H. Lee · Journal of Neurochemistry · 4

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Preclinical Validation Studies Support Causal Machine Learning Based Identification of Novel Drug Targets for High-Risk Multiple Myeloma

2018 · Arnold Bolomsky, Fred Gruber, et al. · Blood · 3

Abstract 5299: Machine learning approach to personalized medicine in breast cancer patients: Development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes

2018 · Henry Kaplan, Anna Berry, et al. · Cancer Research · 2

Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified By Causal Machine Learning: Out-of-Cohort and Experimental Validation

2017 · Leon Furchtgott, Arnold Bolomsky, et al. · Blood · 2

Metabolic Engineering

2011 · D.M. Wuest, S. Hou, et al. · Elsevier eBooks · 2

Causal modeling of CALGB/SWOG 80405 (Alliance) identifies primary (1°) side-related angiogenic drivers of metastatic colorectal cancer (mCRC)

2018 · R.K. Das, L. Furchtgott, et al. · Annals of Oncology · 1

Statistical modeling of CALGB 80405 (Alliance) to identify influential factors in metastatic colorectal cancer (CRC) dependent on primary (1o) tumor side.

2017 · Leon Furchtgott, David Swanson, et al. · Journal of Clinical Oncology · 1

Development and application of an in vitro blood-brain barrier model to investigate intravenous immunoglobulin therapy for Alzheimer's disease

2024 · Wuest, Diane Marie · Library, Museums and Press - UDSpace (University of Delaware)

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Causal modeling of CALGB 80405 (Alliance) to identify network drivers of metastatic colorectal cancer (CRC).

2018 · Rahul K Das, Leon Furchtgott, et al. · Journal of Clinical Oncology

P4–340: The relationship among intravenous immunoglobulin, the blood‐brain barrier and Alzheimer's disease

2013 · Diane Wuest, Kelvin Lee · Alzheimer s & Dementia

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Works count: 18

Citation count: 392

H-index: 7

I10-index: 6