As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs.
Bibliographical noteKAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was supported in part by the National Key Research and Development Program of China [No. 2021YFF1201200 to JXW], and the National Natural Science Foundation of China under Grants [Nos. 61972423 and U1909208 to JXW], 111 Project [No. B18059 to JXW]. We thank anonymous reviewers for their very valuable suggestions. This work was supported in part by the High Performance
Computing Center of Central South University.
ASJC Scopus subject areas
- Molecular Biology
- Information Systems