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BMC Medicine
Published in: BMC Medicine 1/2022

Open Access 01-12-2022 | Research article

NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data

Authors: Xiaoxiao Cheng, Chong Dai, Yuqi Wen, Xiaoqi Wang, Xiaochen Bo, Song He, Shaoliang Peng

Published in: BMC Medicine | Issue 1/2022

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Abstract

Background

Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge.

Methods

In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions.

Results

Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance.

Conclusions

NeRD’s feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.
Appendix
Available only for authorised users
Footnotes
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Metadata
Title
NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data
Authors
Xiaoxiao Cheng
Chong Dai
Yuqi Wen
Xiaoqi Wang
Xiaochen Bo
Song He
Shaoliang Peng
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-022-02549-0

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