Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes
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...
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| Veröffentlicht in: | Analytical chemistry (Washington) 2018-12, Vol.90 (24), p.14216-14221 |
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| creator | Shi, Huayi Wang, Houyu Meng, Xinyu Chen, Runzhi Zhang, Yishu Su, Yuanyuan He, Yao |
| description | 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%. |
| doi_str_mv | 10.1021/acs.analchem.8b03080 |
| format | Article |
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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%.</description><identifier>ISSN: 0003-2700</identifier><identifier>EISSN: 1520-6882</identifier><identifier>DOI: 10.1021/acs.analchem.8b03080</identifier><identifier>PMID: 30456938</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Artificial intelligence ; Artificial neural networks ; Chemistry ; Data acquisition ; Data acquisition systems ; Deoxyribonucleic acid ; DNA ; Genes ; Information processing ; Machine learning ; Nanoparticles ; Neural networks ; p53 Protein ; Raman spectra ; Silicon ; Silver ; Target recognition ; Training ; Tumor suppressor genes ; Tumors</subject><ispartof>Analytical chemistry (Washington), 2018-12, Vol.90 (24), p.14216-14221</ispartof><rights>Copyright American Chemical Society Dec 18, 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a442t-914cca2323a64d1b4b4b2afa38179d0336af6f96443cef60b6b432e965a446a43</citedby><cites>FETCH-LOGICAL-a442t-914cca2323a64d1b4b4b2afa38179d0336af6f96443cef60b6b432e965a446a43</cites><orcidid>0000-0003-2058-275X ; 0000-0003-1672-4057 ; 0000-0002-5134-9881</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b03080$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.analchem.8b03080$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,776,780,2752,27053,27901,27902,56713,56763</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30456938$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Huayi</creatorcontrib><creatorcontrib>Wang, Houyu</creatorcontrib><creatorcontrib>Meng, Xinyu</creatorcontrib><creatorcontrib>Chen, Runzhi</creatorcontrib><creatorcontrib>Zhang, Yishu</creatorcontrib><creatorcontrib>Su, Yuanyuan</creatorcontrib><creatorcontrib>He, Yao</creatorcontrib><title>Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes</title><title>Analytical chemistry (Washington)</title><addtitle>Anal. 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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). 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| subjects | Artificial intelligence Artificial neural networks Chemistry Data acquisition Data acquisition systems Deoxyribonucleic acid DNA Genes Information processing Machine learning Nanoparticles Neural networks p53 Protein Raman spectra Silicon Silver Target recognition Training Tumor suppressor genes Tumors |
| title | Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes |
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