Author

C. K. Soule

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· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:plant (other)· 1

Frequent Co-Authors

J. H. Cheah· 3 shared A. Lopes· 2 shared G. Traverso· 2 shared K. Ishida· 2 shared A. Ali· 1 shared A. Deik· 1 shared A. Farsidjani· 1 shared A. Gardner· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioengineering

J. H. Cheah

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Author profile →

3 papers

Recent·Most cited

Cancer BiologybioRxivFeb 25, 2023

Screening in serum-derived medium reveals differential response to compounds targeting metabolism

K. L. Abbott, A. Ali, D. Casalena…·M. G. Vander Heiden

The paper screens compounds targeting metabolism in serum-derived medium to identify differential responses in cancer cells, aiming to improve drug response prediction.

DOI 10.1016/j.chembiol.2023.08.007

humanbulk RNA-seq

𝕏 PostBluesky

BioengineeringbioRxivPreprintDec 15, 2022

Drinkable, liquid in situ-forming and tough hydrogels for gastrointestinal therapeutics

G. W. Liu, M. J. Pickett, J. L. P. Kuosmanen…·G. Traverso

This paper develops LIFT hydrogels that form in the stomach to deliver drugs with controlled release, improving bioavailability and reducing toxicity.

DOI 10.1101/2022.12.15.520584

ratplant (other)

𝕏 PostBluesky

BioengineeringbioRxivSep 30, 2019

Computationally guided high-throughput design of self-assembling drug nanoparticles

D. Reker, Y. Rybakova, A. R. Kirtane…·G. Traverso

This paper develops a high-throughput screening platform with machine learning to design self-assembling drug nanoparticles, identifying 101 novel formulations from 2.1 million combinations.

DOI 10.1038/s41565-021-00870-y

machine learning (classical)drug response / drug discovery

𝕏 PostBluesky