Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity

These findings indicate that the sensitivity prediction model constructed through the integration of DEG and machine learning analyses can forecast drug sensitivity, thereby contributing to the advancement of effective and personalized precision medicine in classical chemotherapies.

Authors

Honami Yonezawa
Naoyuki Nishiya (ORCID: https://orcid.org/0000-0003-1575-9944)
Kei Oya
Masao Ôta (ORCID: https://orcid.org/0000-0002-6400-5658)
Yuto Sugawara
Tomoko Sakyo
Haruki Ujiie (ORCID: https://orcid.org/0000-0001-7588-0266)

Institutions

Iwate Medical University (JP)

Publication Details

Journal: Cancer Medicine
Published: 2025-11-01
DOI: https://doi.org/10.1002/cam4.71373
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Promotion and Mutual Aid Corporation for Private Schools of Japan

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article

Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity

Honami Yonezawa, Naoyuki Nishiya, Kei Oya, Masao Ôta et al.

Nov 1, 2025

Cancer Medicine

Computational Drug Discovery Methods

article

Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity

Honami Yonezawa, Naoyuki Nishiya, Kei Oya, Masao Ôta, Yuto Sugawara, Tomoko Sakyo, Haruki Ujiie

November 1, 2025article en

Abstract

These findings indicate that the sensitivity prediction model constructed through the integration of DEG and machine learning analyses can forecast drug sensitivity, thereby contributing to the advancement of effective and personalized precision medicine in classical chemotherapies.

Cancer MedicineVol. 14(22)

Iwate Medical University (JP)

Promotion and Mutual Aid Corporation for Private Schools of Japan

Openalex Percentile: Top 64%

Computational Drug Discovery Methods

DOI Open Access PDF OpenAlex

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