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Deep learning-based Integration of Multi-omics Data for Personalized Drug Response Prediction

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Abstract
Individual variations in drug response pose a significant challenge to effective cancer treatment. Our study presents a predictive model that uses multi-omics data to describe drug responses according to individual patient profiles. The model’s main idea is the integration of individual gene sets for each cell line, capturing the distinct pathological signatures of different diseases. It integrates gene expression and DNA methylation data, taking into account the well-established inverse correlation between these factors (where methylation often down-regulates gene expression), to provide insight into the epigenetic mechanisms that influence drug response. Furthermore, we investigated the efficacy of integrating multi-omics data for drug response prediction by applying transformer models developed in multimodal tasks. Finally, our model outperformed state-of-the-art methods in predicting drug response, demonstrating the potential of our approach to contribute to personalised cancer therapy. This advance supports the goal of developing treatments that are specific to an individual’s genetic profile.
Author(s)
Juyoung Kang
Issued Date
2024
Type
Thesis
URI
https://scholar.gist.ac.kr/handle/local/19062
Alternative Author(s)
강주영
Department
대학원 AI대학원
Advisor
Lee, Hyunju
Degree
Master
Appears in Collections:
Department of AI Convergence > 3. Theses(Master)
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