Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellu...

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Bibliographic Details
Main Authors: Patrick C. Kinnunen, Kenneth K. Y. Ho, Siddhartha Srivastava, Chengyang Huang, Wanggang Shen, Krishna Garikipati, Gary D. Luker, Nikola Banovic, Xun Huan, Jennifer J. Linderman, Kathryn E. Luker
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Systems Biology
Subjects:
inverse reinforcment learning
mechanistic modeling
machine learning
cellular heterogeneity
live-cell microscopy
Online Access:https://www.frontiersin.org/articles/10.3389/fsysb.2024.1333760/full

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https://www.frontiersin.org/articles/10.3389/fsysb.2024.1333760/full

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