BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Guselnikova O, Trelin A, Skvortsova A, Ulbrich P, Postnikov P, Pershina A, Sykora D, Svorcik V, Lyutakov O. Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage. Biosens Bioelectron 2019;145:111718. [PMID: 31561094 DOI: 10.1016/j.bios.2019.111718] [Cited by in Crossref: 14] [Cited by in F6Publishing: 23] [Article Influence: 4.7] [Reference Citation Analysis]
Number Citing Articles
1 Li H, Zhang S, Zhu R, Zhou Z, Xia L, Lin H, Chen S. Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2022;278:121314. [PMID: 35525180 DOI: 10.1016/j.saa.2022.121314] [Reference Citation Analysis]
2 Erzina M, Trelin A, Guselnikova O, Skvortsova A, Strnadova K, Svorcik V, Lyutakov O. Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach. Sensors and Actuators B: Chemical 2022;367:132057. [DOI: 10.1016/j.snb.2022.132057] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Moon G, Lee J, Lee H, Yoo H, Ko K, Im S, Kim D. Machine learning and its applications for plasmonics in biology. Cell Reports Physical Science 2022;3:101042. [DOI: 10.1016/j.xcrp.2022.101042] [Reference Citation Analysis]
4 Seweryn S, Skirlińska-Nosek K, Wilkosz N, Sofińska K, Perez-Guaita D, Oćwieja M, Barbasz J, Szymoński M, Lipiec E. Plasmonic hot spots reveal local conformational transitions induced by DNA double-strand breaks. Sci Rep 2022;12:12158. [PMID: 35840615 DOI: 10.1038/s41598-022-15313-4] [Reference Citation Analysis]
5 Vernuccio F, Bresci A, Cimini V, Giuseppi A, Cerullo G, Polli D, Valensise CM. Artificial Intelligence in Classical and Quantum Photonics. Laser & Photonics Reviews 2022;16:2100399. [DOI: 10.1002/lpor.202100399] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
6 Xu X, Aggarwal D, Shankar K. Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions. Nanomaterials 2022;12:633. [DOI: 10.3390/nano12040633] [Reference Citation Analysis]
7 Skvortsova A, Trelin A, Kriz P, Elashnikov R, Vokata B, Ulbrich P, Pershina A, Svorcik V, Guselnikova O, Lyutakov O. SERS and advanced chemometrics – Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment. Analytica Chimica Acta 2022;1192:339373. [DOI: 10.1016/j.aca.2021.339373] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
8 Kozik A, Pavlova M, Petrov I, Bychkov V, Kim L, Dorozhko E, Cheng C, Rodriguez RD, Sheremet E. A review of surface-enhanced Raman spectroscopy in pathological processes. Anal Chim Acta 2021;1187:338978. [PMID: 34753586 DOI: 10.1016/j.aca.2021.338978] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
9 Liang JF, Peng C, Li P, Ye QX, Wang Y, Yi YT, Yao ZS, Chen GY, Zhang BB, Lin JJ, Luo Q, Chen X. A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy. Bioinorg Chem Appl 2021;2021:8180154. [PMID: 34777490 DOI: 10.1155/2021/8180154] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
10 Schackart KE 3rd, Yoon JY. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. Sensors (Basel) 2021;21:5519. [PMID: 34450960 DOI: 10.3390/s21165519] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
11 Yu S, Li X, Lu W, Li H, Fu YV, Liu F. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens. Anal Chem 2021;93:11089-98. [PMID: 34339167 DOI: 10.1021/acs.analchem.1c00431] [Cited by in F6Publishing: 9] [Reference Citation Analysis]
12 Davidovic LM, Laketic D, Cumic J, Jordanova E, Pantic I. Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage. Chem Biol Interact 2021;345:109533. [PMID: 34051207 DOI: 10.1016/j.cbi.2021.109533] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
13 Burtsev V, Erzina M, Guselnikova O, Miliutina E, Kalachyova Y, Svorcik V, Lyutakov O. Detection of trace amounts of insoluble pharmaceuticals in water by extraction and SERS measurements in a microfluidic flow regime. Analyst 2021;146:3686-96. [PMID: 33955973 DOI: 10.1039/d0an02360d] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
14 Banerjee A, Maity S, Mastrangelo CH. Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors. Sensors (Basel) 2021;21:1253. [PMID: 33578726 DOI: 10.3390/s21041253] [Cited by in F6Publishing: 12] [Reference Citation Analysis]
15 Li D, Zhang Q, Deng B, Chen Y, Ye L. Rapid, sensitive detection of ganciclovir, penciclovir and valacyclovir-hydrochloride by artificial neural network and partial least squares combined with surface enhanced Raman spectroscopy. Applied Surface Science 2021;539:148224. [DOI: 10.1016/j.apsusc.2020.148224] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
16 Zhu T, Sun Y, Li C, Xia Y, Wang G, Lu W, Shao M, Man B, Yang C. Film wrap nanoparticle system with the graphene nano-spacer for SERS detection. Opt Express 2021;29:1360-70. [PMID: 33726353 DOI: 10.1364/OE.410603] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
17 Zhao F, Wang W, Zhong H, Yang F, Fu W, Ling Y, Zhang Z. Robust quantitative SERS analysis with Relative Raman scattering intensities. Talanta 2021;221:121465. [DOI: 10.1016/j.talanta.2020.121465] [Cited by in Crossref: 3] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
18 Meza Ramirez CA, Greenop M, Ashton L, Rehman IU. Applications of machine learning in spectroscopy. Applied Spectroscopy Reviews 2021;56:733-63. [DOI: 10.1080/05704928.2020.1859525] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
19 Cui F, Yue Y, Zhang Y, Zhang Z, Zhou HS. Advancing Biosensors with Machine Learning. ACS Sens 2020;5:3346-64. [PMID: 33185417 DOI: 10.1021/acssensors.0c01424] [Cited by in Crossref: 70] [Cited by in F6Publishing: 75] [Article Influence: 35.0] [Reference Citation Analysis]
20 Moon G, Choi J, Lee C, Oh Y, Kim KH, Kim D. Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials. Biosensors and Bioelectronics 2020;164:112335. [DOI: 10.1016/j.bios.2020.112335] [Cited by in Crossref: 9] [Cited by in F6Publishing: 17] [Article Influence: 4.5] [Reference Citation Analysis]
21 Qi G, Wang D, Li C, Ma K, Zhang Y, Jin Y. Plasmonic SERS Au Nanosunflowers for Sensitive and Label-Free Diagnosis of DNA Base Damage in Stimulus-Induced Cell Apoptosis. Anal Chem 2020;92:11755-62. [PMID: 32786448 DOI: 10.1021/acs.analchem.0c01799] [Cited by in Crossref: 7] [Cited by in F6Publishing: 13] [Article Influence: 3.5] [Reference Citation Analysis]
22 Ionescu RE, Poggesi S, Zhou L, Casari Bariani G, Mittapalli R, Adam P, Manzano M. Surface enhanced Raman spectroscopy phylogenetic tree for genosensing of Brettanomyces bruxellensis yeast on nanostructured ultrafine glass supports. Optik 2020;203:163956. [DOI: 10.1016/j.ijleo.2019.163956] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Sofińska K, Wilkosz N, Szymoński M, Lipiec E. Molecular Spectroscopic Markers of DNA Damage. Molecules 2020;25:E561. [PMID: 32012927 DOI: 10.3390/molecules25030561] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
24 Sun J, Tárnok A, Su X. Deep Learning-Based Single-Cell Optical Image Studies. Cytometry A 2020;97:226-40. [PMID: 31981309 DOI: 10.1002/cyto.a.23973] [Cited by in Crossref: 9] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]