Author

Shyam Sundaram

The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:cell type annotation· 1 Assay:electronic health records· 1 Approach:experimental assay· 1 Assay:flow cytometry· 1 Research Focus:host-pathogen & immune response· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Assay:proteomics· 1

Frequent Co-Authors

Anatol BraginiD· 1 shared Balázs BognáriD· 1 shared David E. CohniD· 1 shared David M. O’MalleyiD· 1 shared Farshad Kheiri· 1 shared Gabriela S. Vendrell· 1 shared Ganesh YadagiriiD· 1 shared Jessica Velasquez· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioinformatics

Anatol BraginORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

HO-3867 mediated modulation of Extracellular vesicles and Tumor microenvironment: A novel immunotherapeutic strategy for ovarian cancer

Ganesh Yadagiri, Lakshmi Narasimhan Chakrapani, Shyam Sundaram, Jessica Velasquez, Tiffany L. Hughes, Gabriela S. Vendrell, Balázs Bognár, Qi‐En Wang, David M. O’Malley, David E. Cohn, Karuppaiyah Selvendiran, Kalpana Deepa Priya Dorayappan

This study shows that HO-3867 and bevacizumab together reduce ascites, limit tumor spread, and enhance anti-tumor immunity in ovarian cancer models by modulating extracellular vesicles and immune cells.

bioRxiv · May 16, 2025 · PreprintCancer Biology

Nonlinear EEG Analysis for Distinguishing Mind Wandering and Focused Attention: A Machine Learning Approach

Farshad Kheiri, Shyam Sundaram, Anatol Bragin

This paper uses nonlinear EEG analysis and machine learning to classify mind wandering and focused attention states with 75% accuracy using 5-second segments.

bioRxiv · Oct 18, 2024 · PreprintBioinformatics