Enhancing Cancer Immunotherapy Response Prediction using Multi-omics Integration and Deep Learning

Authors

  • Jambi Ratna Raja Kumar Associate Professor, Computer Engineering department, , Sr.No.25/1/3, Balewadi, Pune -411045, Genba Sopanrao Moze College of Engineering
  • Dharmesh Dhabliya Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
  • Anishkumar Dhablia Engineering Manager, Altimetrik India Pvt Ltd, Pune, Maharashtra, India

Keywords:

data integration, multi-omics, integration strategies

Abstract

Cancer immunotherapy has emerged as a promising approach to treat various malignancies by harnessing the patient's immune system to target cancer cells. However, the success of immunotherapy varies significantly among patients due to the complex and heterogeneous nature of the tumor microenvironment. To address this challenge, a novel machine learning approach is proposed to predict the response to cancer immunotherapy, utilizing a combination of multi-omics data integration and deep learning techniques.

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References

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Published

16.07.2023

How to Cite

Raja Kumar, J. R. ., Dhabliya, D. ., & Dhablia, A. . (2023). Enhancing Cancer Immunotherapy Response Prediction using Multi-omics Integration and Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 426–434. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3184

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Section

Research Article

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