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Updated: Jan 15, 2026

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
Published on: December 1, 2023
Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps
Zahid Rafiq1,2, Tanzeel Bashir3, Weiqin Lu2
1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States.
Abstract:
The limited translatability of animal models can be significantly amplified by integration of Artificial Intelligence (AI) and Machine Learning (ML). This Viewpoint represents a fresh paradigm in pharmacology and translational science, one that accelerates hypothesis testing, reduces resource burden, and improves clinical predictability. By aligning computational precision with experimental rigor, this integrated approach provides more ethical, scalable, and personalized cancer therapeutics.

