Integration of Machine Learning Algorithms for Predictive Modeling of Drug Response in Cancer Patients: A Multi-omics Approach

Shangyi Wang^*
*Correspondence: Shangyi Wang, Department of Pharmaceutical Sciences, College of Pharmacy, Ewha Womans University, Seoul 03760, Republic of Korea, Email:

Introduction

Cancer remains one of the leading causes of mortality worldwide, with diverse genetic and molecular profiles contributing to variable treatment responses. Traditional approaches to cancer treatment often fail to account for the individual variability in drug response, leading to suboptimal outcomes. The advent of multi-omics technologies, which encompass genomic, transcriptomic, and proteomic data, provides a wealth of information that can be harnessed to understand the complex mechanisms underlying drug response. Machine learning, with its capability to handle large and complex datasets, offers a powerful tool for integrating multi-omics data to predict drug response. This study aims to develop and validate machine learning models for predicting drug response in cancer patients using multi-omics data, with the goal of advancing personalized cancer treatment [1].

Description

To develop predictive models for drug response, we collected comprehensive multi-omics datasets from cancer patients, including genomic (DNA sequencing), transcriptomic (RNA sequencing), and proteomic (protein expression) data. These datasets were sourced from publicly available repositories such as The Cancer Genome Atlas (TCGA) and other clinical databases. The integration of these datasets provided a holistic view of the molecular landscape of each patient [2]. We employed a range of machine learning algorithms, including random forests, support vector machines, and neural networks, to build predictive models. The data preprocessing steps involved normalization, feature selection, and dimensionality reduction techniques such as principal component analysis (PCA) to enhance model performance and reduce computational complexity [3]. The models were trained on a subset of the data and validated using cross-validation techniques to ensure robustness and prevent overfitting. Key performance metrics, including accuracy, precision, recall, and the area under the receiver operating characteristic curve, were used to evaluate model performance. Feature importance scores were also analyzed to identify key biomarkers associated with drug response [4,5].

Conclusion

This study demonstrates the efficacy of integrating machine learning algorithms with multi-omics data to predict drug response in cancer patients. The developed models provide a robust framework for identifying key biomarkers and optimizing treatment strategies, advancing the field of precision medicine. While challenges persist, the continued evolution of multi-omics technologies and machine learning methodologies holds great promise for improving personalized cancer care. Future research should focus on expanding the dataset, incorporating additional omics layers, and validating the models in clinical settings to further enhance their applicability and reliability.

Acknowledgement

None.

Conflict of Interest

None.

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