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PreprintCC BYbioRxiv

Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning

  • K. Hu
  • Y. Zhang
  • L. Feng
  • Q. Yang
  • Z. Li
  • P. Pan
  • F. He1
  1. 1In Vitro Biology, Pharmaron Beijing Co., Ltd.
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Abstract

Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset, particularly at higher initial seeding densities. We further extended the RF framework to predict growth curves across seeding densities. Collectively, these findings highlight the potential of data-driven modeling to complement conventional mathematical approaches and support more informed cell-culture design.

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Cite

BibTeXRIS
BibTeX
@misc{hu2026predictive,
  title = {{Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning}},
  author = {K. Hu and Y. Zhang and L. Feng and Q. Yang and Z. Li and P. Pan and F. He},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.19.752932},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.19.752932v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning
AU  - K. Hu
AU  - Y. Zhang
AU  - L. Feng
AU  - Q. Yang
AU  - Z. Li
AU  - P. Pan
AU  - F. He
PY  - 2026
DA  - 2026/09/19
PB  - bioRxiv
DO  - 10.64898/2026.09.19.752932
UR  - https://www.biorxiv.org/content/10.64898/2026.09.19.752932v1
AB  - Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset, particularly at higher initial seeding densities. We further extended the RF framework to predict growth curves across seeding densities. Collectively, these findings highlight the potential of data-driven modeling to complement conventional mathematical approaches and support more informed cell-culture design.
ER  -