DRN-CDR: A cancer drug response prediction model using multi-omics and drug features.
Autor: | Saranya KR; Department of Computer Science and IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India., Vimina ER; Department of Computer Science and IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India. Electronic address: vimina.er@gmail.com. |
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Jazyk: | angličtina |
Zdroj: | Computational biology and chemistry [Comput Biol Chem] 2024 Oct; Vol. 112, pp. 108175. Date of Electronic Publication: 2024 Aug 21. |
DOI: | 10.1016/j.compbiolchem.2024.108175 |
Abstrakt: | Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (r Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier Ltd. All rights reserved.) |
Databáze: | MEDLINE |
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