Network-based drug sensitivity prediction
| Autor: | Yunku Yeu, Sunho Park, Qibing Jiang, Tae Hyun Hwang, Wei Zhang, Khandakar Tanvir Ahmed |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Elastic net regularization
Gene co-expression network lcsh:Internal medicine Lung Neoplasms lcsh:QH426-470 Computer science Gene regulatory network Feature selection Antineoplastic Agents computer.software_genre Drug sensitivity prediction Deep Learning Carcinoma Non-Small-Cell Lung Genetics Feature (machine learning) Biomarkers Tumor Humans Gene Regulatory Networks lcsh:RC31-1245 Genetics (clinical) Network embedding Artificial neural network Gene Expression Profiling Research Graph-based neural network Computational Biology Prognosis Random forest Support vector machine Gene Expression Regulation Neoplastic lcsh:Genetics Network-based feature selection Data mining Neural Networks Computer computer Software |
| Zdroj: | BMC Medical Genomics, Vol 13, Iss S11, Pp 1-10 (2020) BMC Medical Genomics |
| ISSN: | 1755-8794 |
| Popis: | BackgroundDrug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study.MethodsIn this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available athttps://github.com/compbiolabucf/drug-sensitivity-prediction.ResultsIn the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug’s mechanism of action.ConclusionsNetwork-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets. |
| Databáze: | OpenAIRE |
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