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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models.

Dyuti Banerjee¹, Sivaneasan Bala Krishnan², Kamal Upreti³

¹Koneru Lakshmaiah Education Foundation.

Journal of Visualized Experiments : Jove

|September 29, 2025

Summary

This summary is machine-generated.

This study developed a machine learning protocol to predict breast cancer drug sensitivity and identify effective drug combinations. The XGBoost model accurately predicted drug responses, aiding precision oncology.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Computational biology
Genomics
Pharmacology

Background:

Breast cancer presents significant treatment challenges due to tumor heterogeneity and drug resistance.
Predicting drug sensitivity and identifying synergistic combinations is crucial for effective breast cancer therapy.

Purpose of the Study:

To develop and validate a data-driven machine learning protocol for predicting drug sensitivity in breast cancer.
To identify potent single agents and synergistic drug combinations for breast cancer treatment.
To provide a reproducible and interpretable framework for precision oncology.

Main Methods:

Utilized curated Genomics of Drug Sensitivity in Cancer (GDSC) datasets.
Implemented a standalone XGBoost regressor and a hybrid Autoencoder-XGBoost model.
Applied preprocessing techniques including encoding, standardization, imputation, and PCA for dimensionality reduction.
Employed SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

The standalone XGBoost model outperformed the hybrid approach, achieving a high R-squared value (0.8145).
SHAP analysis identified key predictive features such as TARGET_PATHWAY, DRUG_ID, TARGET, and CELL_LINE_NAME.
Predicted synergy scores highlighted promising drug combinations, including Bortezomib + Romidepsin and Paclitaxel + Bortezomib.

Conclusions:

The developed machine learning protocol offers a transparent and adaptable framework for precision oncology research.
The model demonstrates both predictive accuracy and biological interpretability, facilitating drug discovery and repurposing.
This workflow provides a scalable foundation for advancing breast cancer treatment strategies.