期刊
ANALYTICAL CHEMISTRY
卷 90, 期 24, 页码 14216-14221出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.8b03080
关键词
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资金
- National Basic Research Program of China [2013CB934400]
- National Natural Science Foundation of China [61361160412, 31400860, 21575096, 21605109]
- 111 Project
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
The quality of input data in deep learning is tightly associated with the ultimate performance of the machine learner. Taking advantage of the unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of a database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up a SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications. The database is collected and analyzed by silver nanoparticles (Ag NPs)-decorated silicon wafer (Ag NPs@Si) SERS chip, followed by training with a deep neural network (DNN). As proof-of-concept applications, three kinds of representative tumor suppressor genes, i.e., p16, p21, and p53 fragments, are readily discriminated in a label-free manner. Prominent and reproducible SERS spectra of these DNA molecules are collected and employed as input data for DNN learning and training, which enables selective discrimination of DNA target(s). The accuracy rate for the recognition of specific DNA target reached 90.28%.
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