Chandrasekaran S - Search Results

1

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.

Cicchese JM, Sambarey A, Kirschner D, Linderman JJ, Chandrasekaran S. Cicchese JM, et al. Among authors: chandrasekaran s. Sci Rep. 2021 Mar 11;11(1):5643. doi: 10.1038/s41598-021-84827-0. Sci Rep. 2021. PMID: 33707554 Free PMC article.

2

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis.

Ma S, Jaipalli S, Larkins-Ford J, Lohmiller J, Aldridge BB, Sherman DR, Chandrasekaran S. Ma S, et al. Among authors: chandrasekaran s. mBio. 2019 Nov 12;10(6):e02627-19. doi: 10.1128/mBio.02627-19. mBio. 2019. PMID: 31719182 Free PMC article.

3

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Cantrell JM, Chung CH, Chandrasekaran S. Cantrell JM, et al. Among authors: chandrasekaran s. Drug Discov Today. 2022 Jun;27(6):1639-1651. doi: 10.1016/j.drudis.2022.04.006. Epub 2022 Apr 7. Drug Discov Today. 2022. PMID: 35398560 Review.

4

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.

Sambarey A, Smith K, Chung C, Arora HS, Yang Z, Agarwal P, Chandrasekaran S. Sambarey A, et al. Among authors: chandrasekaran s. medRxiv [Preprint]. 2022 Jul 21:2022.07.20.22277862. doi: 10.1101/2022.07.20.22277862. medRxiv. 2022. PMID: 35898335 Free PMC article. Preprint.

5

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.

Sambarey A, Smith K, Chung C, Arora HS, Yang Z, Agarwal PP, Chandrasekaran S. Sambarey A, et al. Among authors: chandrasekaran s. iScience. 2024 Jan 29;27(2):109025. doi: 10.1016/j.isci.2024.109025. eCollection 2024 Feb 16. iScience. 2024. PMID: 38357663 Free PMC article.

6

Predicting Drug Interactions From Chemogenomics Using INDIGO.

Chandrasekaran S. Chandrasekaran S. Methods Mol Biol. 2019;1888:219-231. doi: 10.1007/978-1-4939-8891-4_13. Methods Mol Biol. 2019. PMID: 30519950

7

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.

Cokol M, Li C, Chandrasekaran S. Cokol M, et al. Among authors: chandrasekaran s. PLoS Comput Biol. 2018 Dec 31;14(12):e1006677. doi: 10.1371/journal.pcbi.1006677. eCollection 2018 Dec. PLoS Comput Biol. 2018. PMID: 30596642 Free PMC article.

8

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.

Chung CH, Chandrasekaran S. Chung CH, et al. Among authors: chandrasekaran s. PNAS Nexus. 2022 Jul 22;1(3):pgac132. doi: 10.1093/pnasnexus/pgac132. eCollection 2022 Jul. PNAS Nexus. 2022. PMID: 36016709 Free PMC article.

9

Chemogenomics and orthology-based design of antibiotic combination therapies.

Chandrasekaran S, Cokol-Cakmak M, Sahin N, Yilancioglu K, Kazan H, Collins JJ, Cokol M. Chandrasekaran S, et al. Mol Syst Biol. 2016 May 24;12(5):872. doi: 10.15252/msb.20156777. Mol Syst Biol. 2016. PMID: 27222539 Free PMC article.

10

Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling.

Chandrasekaran S, Zhang J, Sun Z, Zhang L, Ross CA, Huang YC, Asara JM, Li H, Daley GQ, Collins JJ. Chandrasekaran S, et al. Cell Rep. 2017 Dec 5;21(10):2965-2977. doi: 10.1016/j.celrep.2017.07.048. Cell Rep. 2017. PMID: 29212039 Free PMC article.

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