ArticleAsian Pacific journal of cancer prevention : APJCP2025
Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.
Article in Asian Pacific journal of cancer prevention : APJCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Bioinformatic Approach to Identify PotentialInternational journal of molecular sciences · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
objectiveOvarian cancer's complexity and heterogeneity pose significant challenges in treatment, often resulting in suboptimal chemotherapy outcomes. This study aimed to leverage machine learning algorithms, gene selection, and gene expression data to improve chemotherapy results.
methodsThe mutual_info_classif approach was employed to identify the most informative genes for predicting treatment responses. Ten machine learning techniques were used to assess and optimize the predictive potential of these genes.
resultBy examining the reciprocal relationships between gene expression and chemotherapy outcomes, the study identified a subset of 20 critical genes essential for treatment efficacy. Among the selected genes, the Random Forest classifier demonstrated the highest accuracy, achieving 97% accuracy, 98% precision, 97% recall, and a 97.5% F1-score in predicting treatment responses. With statistical significance (p = 0.019), the carboplatin predictor successfully distinguished between platinum-sensitive and platinum-resistant patients. Additionally, the combined predictor for the platinum-taxane regimen revealed a significant difference in survival between predicted responders and non-responders, with median survival times of 12.9 months and 8.1 months, respectively (p < 0.045).
conclusionThe exceptional performance of this model highlights its ability to integrate complex gene expression data, facilitating the development of personalized chemotherapy regimens.
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Registered trials
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