Author

Yitong Huang

Smith CollegeORCID 0000-0002-5200-8077Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yitong Huang1 paper · Shanghai University of Traditional Chinese Medicine

Biology Disciplines

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Systems Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Organism:human· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

Bingxian XuiD· 1 shared Cathy A. GoldsteiniD· 1 shared Clark Bowman· 1 shared Daniel B. ForgeriD· 1 shared Eduardo D. SontagiD· 1 shared Elena FrankiD· 1 shared Harsh Vardhan JainiD· 1 shared Irina KarevaiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperBioinformatics

Bingxian XuORCID

Author profile →

3 papers

Recent·Most cited

Delaying Cancer Progression by Integrating Toxicity Constraints in a Model of Adaptive Therapy

Jana L. Gevertz, Harsh Vardhan Jain, Irina Kareva, Kathleen P. Wilkie, Joel S. Brown, Yitong Huang, Eduardo D. Sontag, Vladimir Vinogradov, Mark Davies

This paper develops a mathematical model to assess how incorporating toxicity feedback affects adaptive cancer therapy, finding that toxicity-induced treatment breaks can extend time to progression.

npj Systems Biology and Applications · Jan 7, 2026Peer-reviewedSystems Biology

Time-series modeling with neural flow maps

Bingxian Xu, Zoey E. Ho, Yitong Huang

This work introduces a deep learning framework to reconstruct flow maps from time-series data, demonstrating its effectiveness in capturing circadian transcriptomic dynamics across multiple organs.

bioRxiv · Jun 18, 2025 · PreprintBioinformatics

Characterizing the Human Heart Rate Circadian Pacemaker through Widely Available Wearable Devices

Clark Bowman, Yitong Huang, Olivia Walch, Yu Fang, Elena Frank, Cathy A. Goldstein, Srijan Sen, Daniel B. Forger

The paper extracts circadian rhythms in heart rate from wearable data to study individual variations and real-world stimuli effects.

bioRxiv · Mar 12, 2020 · PreprintSystems Biology