A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth

Helena Coggan, Helena Andres Terre, Pietro Liò

Open source

DOI: 10.3389/fdata.2022.941451
Published: 2022-09-12
Container: Frontiers in Big Data
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fdata.2022.941451,
  title = {A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth},
  author = {Helena Coggan and Helena Andres Terre and Pietro Liò},
  year = {2022},
  journal = {Frontiers in Big Data},
  doi = {10.3389/fdata.2022.941451},
  url = {https://doi.org/10.3389/fdata.2022.941451}
}

RIS

TY  - JOUR
TI  - A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth
AU  - Helena Coggan
AU  - Helena Andres Terre
AU  - Pietro Liò
PY  - 2022
JO  - Frontiers in Big Data
DO  - 10.3389/fdata.2022.941451
UR  - https://doi.org/10.3389/fdata.2022.941451
ER  -

APA

Coggan, H., Terre, H. A., & Liò, P. (2022). A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth. Frontiers in Big Data. https://doi.org/10.3389/fdata.2022.941451

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crossref · retrieved 2026-09-25T06:06:40.938Z