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A multi-view multi-omics model for cancer drug response prediction

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Abstract

Cancer drug response prediction is the fundamental task in precision medicine, which provides opportunities for cancer therapy. Several methods have been proposed to screen drugs, via building computational models on multi-omics data. However, the view value missing problem caused by unknown cancers or tumors has not been addressed. For this reason, a multi-view multi-omics (MvMo) model is proposed to predict cancer drug response values. The proposed MvMo model first represents the input heterogeneous data in different kinds of embeddings and features, such as token embeddings and latent features. Then several views are generated to observe interconnections among those representations. Finally, the predictions are generated based on the outputs of these views. Experimental results on the collected real data show the efficiency of the proposed method in terms of speed and accuracy.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://www.cancerrxgene.org/

  2. https://sites.broadinstitute.org/ccle

  3. https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

  4. https://pubchem.ncbi.nlm.nih.gov/

  5. http://github.com/summatic/CDRScan

  6. http://github.com/kimmo1019/DeepCDR

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Acknowledgements

This work was supported in part by Jimei University (no. ZP2021013), the Science project of Xiamen City (no. 3502Z20193048), the Education Department of Fujian Province (CN) (no. JAT200277), and the Natural Science Foundation of Fujian Province (CN) (no. 2021J01859).

We would like to thank the editor and anonymous reviewers for their helpful comments in improving the manuscript quality.

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Authors and Affiliations

  1. Computer Engineering College, Jimei University, Yinjiang Road 185, Xiamen, 361021, China

    Zhijin Wang, Ziyang Wang & Yonggang Fu

  2. College of Electronic Information, Guangxi University for Nationalities, Daxue East Road 188, Nanning, 530006, China

    Yaohui Huang

  3. School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China

    Longquan Lu

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  1. Zhijin Wang
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Correspondence to Yonggang Fu.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Wang, Z., Wang, Z., Huang, Y. et al. A multi-view multi-omics model for cancer drug response prediction. Appl Intell 52, 14639–14650 (2022). https://doi.org/10.1007/s10489-022-03294-w

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