Chandrasekaran, Sriram

Dissecting Murine Muscle Stem Cell Aging through Regeneration Using Integrative Genomic Analysis

Shcherbina, A., Larouche, J., Fraczek, P., Yang, B. A., Brown, L. A., Markworth, J. F., Chung, C. H., Khaliq, M., de Silva, K., Choi, J. J., et al. (2020) · Cell Reports

Muscle Physiology and DisordersCongenital heart defects research

Cited by 76

Transcriptional regulatory dynamics drive coordinated metabolic and neural response to social challenge in mice

Saul, M. C., Seward, C. H., Troy, J. M., Zhang, H., Sloofman, L. G., Lu, X., Weisner, P. A., Caetano-Anolles, D., Sun, H., Zhao, S. D., et al. (2017) · Genome Research

Genomics and Chromatin DynamicsStress Responses and Cortisol

Cited by 46

Metabolic signatures of regulation by phosphorylation and acetylation

Smith, K., Shen, F., Lee, H. J., & Chandrasekaran, S. (2019) · iScience

Microbial Metabolic Engineering and BioproductionGene Regulatory Network Analysis

Cited by 20

Rapid prediction of thermodynamically destabilizing tyrosine phosphorylations in cancers

Woodard, J., Liu, Z., Chegini, A. M., Tian, J., Bhowmick, R., Pennathur, S., Mashaghi, A., Brender, J., & Chandrasekaran, S. (2024) · Cell Reports Methods

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 4

A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations

Woodard, J., Liu, Z., Chegini, A. M., Tian, J., Bhowmick, R., Pennathur, S., Mashaghi, A., Brender, J. R., & Chandrasekaran, S. (2025) · Cell Reports Methods

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 3

Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling

Schildcrout, R., Rhoads, N., & Chandrasekaran, S. (2026) · npj Metabolic Health and Disease

Microbial Metabolic Engineering and BioproductionGene Regulatory Network Analysis

Cited by 0