Guest Editors
Prof. Dr. Chiou-Hwa Yuh
Email: chyuh@nhri.edu.tw
Affiliation: Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli, 35053, Taiwan
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Research Interests: AI-guided phenotypic response surfaces, precision dosing, combination therapy, MASLD-HCC, zebrafish models, functional phenotyping, spatial/single-cell omics, metabolic–immune crosstalk, G-bodies
Summary
The rising complexity of cancer therapy—multi-drug regimens, patient heterogeneity, and evolving resistance—demands quantitative frameworks beyond trial-and-error dosing. Phenotypic Response Surfaces (PRS) and related AI-guided methods learn dose–response landscapes from experimental or clinical data to identify efficacy-optimal, toxicity-aware dose combinations. This Special Issue invites research that advances the methodology, validation, and translation of PRS/AI-PRS in oncology. We welcome (i) algorithmic innovations (global optimization, active learning, Bayesian/robust designs, uncertainty quantification); (ii) model-system studies (cells, organoids, zebrafish, mouse) that map phenotypic landscapes for targeted, immune, or repurposed agents; (iii) biomarker and multi-omics integration to personalize PRS; (iv) bench-to-bedside bridges, including adaptive dose-finding, N-of-1 or basket designs, and early clinical implementation; and (v) standards for rigor—data/model sharing, reproducible pipelines, and reporting checklists. Reviews, original research, brief reports, and resources (datasets, code, benchmarks) are encouraged. By assembling contributions across computation, experiment, and clinical translation, this Special Issue aims to provide a practical playbook for deploying AI-guided PRS to design safer, more effective cancer combination therapies and to accelerate precision dosing in real-world settings.
Keywords
translational oncology, biomarkers & multi-omics integration, combination therapy design, AI-guided dose optimization, phenotypic response surface (PRS)
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