Author

S. G. Moore

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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Bioengineering· 3 Cancer Biology· 2

Signature Assays, Organisms & Methods

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Organism:human· 4 Research Focus:drug response / drug discovery· 3 Approach:machine learning (classical)· 3 Assay:metabolomics· 3 Research Focus:differential expression· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:mouse· 1

Frequent Co-Authors

D. A. Gaul· 2 shared D. DeRyckere· 2 shared D. K. Graham· 2 shared E. C. Dreaden· 2 shared F. O. Fernandez· 2 shared H. Fu· 2 shared H. Zecca· 2 shared J. Jain· 2 shared

Activity by Year

2022-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersCancer Biology

D. A. Gaul

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

S. Sah, O. O. Bifarin, S. G. Moore, D. A. Gaul, H. Chung, H. Cho, C.-H. Cho, J.-H. Kim, J. Kim, F. O. Fernandez

This study identifies a distinct lipidome signature in Korean women with ovarian cancer, showing early-stage alterations and a predictive panel of 17 lipids with 85% accuracy.

bioRxiv · Oct 5, 2023Cancer Biology

Constitutively synergistic multiagent drug formulations targeting MERTK, FLT3, and BCL-2 for treatment of AML

J. M. Kelvin, J. Jain, A. Thapa, M. Qui, L. A. Birnbaum, S. G. Moore, H. Zecca, R. J. Summers, E. Costanza, B. Uricoli, X. Wang, N. T. Jui, H. Fu, Y. Du, D. DeRyckere, , E. C. Dreaden

The paper develops a nanoscale combination drug formulation targeting MERTK, FLT3, and BCL-2 to treat AML, showing improved synergistic responses in vitro and in patient samples.

bioRxiv · Mar 13, 2023 · PreprintBioengineering

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency

A. Van Grouw, M. B. Colonna, T. S. Maughon, X. Shen, A. M. Larey, S. G. Moore, Y. Carolyn, F. M. Facundo, E. S. Arthur, S. L. Stice, B.-W. C. Annie, M. A. Ross

The paper develops a consensus machine learning approach to identify metabolites predictive of mesenchymal stromal cell immunomodulatory function across multiple lines.

bioRxiv · Feb 3, 2023Bioengineering

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

O. O. Bifarin, S. Sah, D. A. Gaul, S. G. Moore, R. Chen, M. Palaniappan, J. Kim, M. M. Matzuk, F. O. Fernandez

This study uses machine learning to analyze lipidome changes in a mouse model of ovarian cancer, identifying early and late-stage lipid alterations associated with cancer progression.

bioRxiv · Jan 4, 2023 · PreprintCancer Biology

Screening and Development of Constitutively Synergistic Combination Drug Formulations for T Cell Acute Lymphoblastic Leukemia

J. M. Kelvin, D. Y. Zhang, E. K. Williams, S. G. Moore, L. A. Birnbaum, H. Zecca, X. Wang, J. Jain, M. Qui, N. T. Jui, H. Fu, Y. Du, M. L. Kemp, W. A. Lam, D. DeRyckere, , E. C. Dreaden

The paper identifies synergistic drug combinations for T-ALL using high-throughput screens and develops lipid nanoparticle formulations that maintain drug synergy and improve anti-leukemic activity.

bioRxiv · Sep 21, 2022 · PreprintBioengineering