Xiaoxiao Cheng1, Chong Dai2,3, Yuqi Wen3, Xiaoqi Wang1, Xiaochen Bo4, Song He5, Shaoliang Peng6,7. 1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. 2. College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China. 3. Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China. 4. Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China. boxiaoc@163.com. 5. Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China. hes1224@163.com. 6. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. slpeng@hnu.edu.cn. 7. The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha, China. slpeng@hnu.edu.cn.
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.
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.
Authors: James C Costello; Laura M Heiser; Elisabeth Georgii; Mehmet Gönen; Michael P Menden; Nicholas J Wang; Mukesh Bansal; Muhammad Ammad-ud-din; Petteri Hintsanen; Suleiman A Khan; John-Patrick Mpindi; Olli Kallioniemi; Antti Honkela; Tero Aittokallio; Krister Wennerberg; James J Collins; Dan Gallahan; Dinah Singer; Julio Saez-Rodriguez; Samuel Kaski; Joe W Gray; Gustavo Stolovitzky Journal: Nat Biotechnol Date: 2014-06-01 Impact factor: 54.908
Authors: Cai Huang; Evan A Clayton; Lilya V Matyunina; L DeEtte McDonald; Benedict B Benigno; Fredrik Vannberg; John F McDonald Journal: Sci Rep Date: 2018-11-06 Impact factor: 4.379