Sriram Chandrasekaran

Assistant Professor

University of Michigan

United States

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About

Dr. Sriram Chandrasekaran is an Assistant Professor at the University of Michigan, USA. His research focuses on metabolic modeling, machine learning applications in cancer therapy, and the analysis of multimodal patient data. Recent publications include studies on metabolic vulnerabilities during epithelial to mesenchymal transition and personalized predictors of tuberculosis treatment prognosis.

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2024

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2021

Collaborators (5)

Manjunath Pai

Professor and Chair

University of Michigan

UNITED STATES

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Margaret Morris

Professor of Pharmacology, Head

UNSW Sydney

AUSTRALIA

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Duxin Sun

UNITED STATES

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Scott Campit

University of Michigan

UNITED STATES

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Venkateshwar Keshamouni

University of Michigan

UNITED STATES

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Sriram Chandrasekaran

Research Interests

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Articles (20)

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

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

Genome-scale modeling identifies dynamic metabolic vulnerabilities during the epithelial to mesenchymal transition

Protocol for performing metabolic pathway-based subtyping of breast tumors

Recon8D: A metabolic regulome network from oct-omics and machine learning.

Abstract PO3-08-07: Licochalcone A as a risk reducing agent against luminal and non-luminal breast cancers

Abstract B051: DNA damage signaling activates GTP synthesis to promote glioblastoma treatment resistance

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

TMET-13. DNA Damage Signaling Activates GTP Synthesis to Promote Glioblastoma Treatment Resistance

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease

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

Deep Learning for Reintegrating Biology

Metabolic signatures of regulation by phosphorylation and acetylation

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

Machine learning to design antimicrobial combination therapies: Promises and pitfalls

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer

TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes

Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective

Collaborators (5)

Manjunath Pai

Margaret Morris

Duxin Sun

Scott Campit

Venkateshwar Keshamouni

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