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PREreview structurée de Integrated Systems Oncology: A Multimodal Framework for Addressing Cancer Heterogeneity

par Isaac Tanimomo, William Idoko, Mabel Bolajoko Omoniwa et Opeoluwa Esther Ajisafe du Review & Curate Network (RCN)

Does the introduction explain the objective of the research presented in the preprint?: Yes; The introduction provides sufficient background that leads to the research objectives.
Are the methods well-suited for this research?: Highly appropriate; A computational data analytical approach was used, which is well suited for gathering principles from genetics, and integration of AI model for cancer Heterogeneity.
Are the conclusions supported by the data?: Highly supported; The conclusions are consistently thorough and provides realistic interpretation of the data used.
Are the data presentations, including visualizations, well-suited to represent the data?: Highly appropriate and clear; The data representations are excellent and well structured.
How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?: Somewhat clearly
Is the preprint likely to advance academic knowledge?: Highly likely; The preprint addresses a knowledge gap on cancer cell resistance, giving a realistic solution.
Would it benefit from language editing?: No
Would you recommend this preprint to others?: Yes, it’s of high quality
Is it ready for attention from an editor, publisher or broader audience?: Yes, after minor changes; The paper is excessively voluminous.

The authors declare that they have no competing interests.

The authors declare that they did not use generative AI to come up with new ideas for their review.

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