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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. Mol Ther Nucleic Acids 2023;31:691-702. [PMID: 36923950 DOI: 10.1016/j.omtn.2023.02.019] [Reference Citation Analysis]
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Entezari M, Yousef Abad GG, Sedghi B, Ettehadi R, Asadi S, Beiranvand R, Haratian N, Karimian SS, Jebali A, Khorrami R, Zandieh MA, Saebfar H, Hushmandi K, Salimimoghadam S, Rashidi M, Taheriazam A, Hashemi M, Ertas YN. Gold nanostructure-mediated delivery of anticancer agents: Biomedical applications, reversing drug resistance, and stimuli-responsive nanocarriers. Environ Res 2023;225:115673. [PMID: 36906270 DOI: 10.1016/j.envres.2023.115673] [Reference Citation Analysis]
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Guo J, Hu J, Zheng Y, Zhao S, Ma J. Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer. Br J Cancer 2023. [PMID: 36871044 DOI: 10.1038/s41416-023-02215-z] [Reference Citation Analysis]
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023;189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Reference Citation Analysis]
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Ahmadi S, Ebrahimi Warkiani M, Rabiee M, Iravani S, Rabiee N. Carbon-based nanomaterials against SARS-CoV-2: Therapeutic and diagnostic applications. OpenNano 2023;10:100121. [DOI: 10.1016/j.onano.2023.100121] [Reference Citation Analysis]
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Sinha K, Uddin Z, Kawsar H, Islam S, Deen M, Howlader M. Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks. TrAC Trends in Analytical Chemistry 2023;158:116861. [DOI: 10.1016/j.trac.2022.116861] [Reference Citation Analysis]
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Chen X, Shu W, Zhao L, Wan J. Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis. VIEW 2022. [DOI: 10.1002/viw.20220038] [Reference Citation Analysis]
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Wu Q, Zhou QH, Li W, Ren TB, Zhang XB, Yuan L. Evolving an Ultra-Sensitive Near-Infrared β-Galactosidase Fluorescent Probe for Breast Cancer Imaging and Surgical Resection Navigation. ACS Sens 2022;7:3829-37. [PMID: 36383027 DOI: 10.1021/acssensors.2c01752] [Reference Citation Analysis]
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Chen X, Lv H. Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning. NPG Asia Mater 2022;14. [DOI: 10.1038/s41427-022-00416-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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Alafeef M, Pan D. Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward. ACS Nano 2022;16:11545-76. [PMID: 35921264 DOI: 10.1021/acsnano.2c01697] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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Moitra P, Alafeef M, Dighe K, Pan D. Single-gene diagnostic assay for rapid subclassification of basal like breast cancer with mRNA targeted antisense oligonucleotide capped molecular probe. Biosensors and Bioelectronics 2022;207:114178. [DOI: 10.1016/j.bios.2022.114178] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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Han YD, Kim KR, Lee KW, Yoon HC. Retroreflection-based optical biosensing: From concept to applications. Biosensors and Bioelectronics 2022;207:114202. [DOI: 10.1016/j.bios.2022.114202] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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Serov N, Vinogradov V. Artificial intelligence to bring nanomedicine to life. Adv Drug Deliv Rev 2022;184:114194. [PMID: 35283223 DOI: 10.1016/j.addr.2022.114194] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
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Chen C, Yaari Z, Apfelbaum E, Grodzinski P, Shamay Y, Heller DA. Merging data curation and machine learning to improve nanomedicines. Adv Drug Deliv Rev 2022;183:114172. [PMID: 35189266 DOI: 10.1016/j.addr.2022.114172] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
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Villa Nova M, Lin TP, Shanehsazzadeh S, Jain K, Ng SCY, Wacker R, Chichakly K, Wacker MG. Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence. Front Digit Health 2022;4:799341. [DOI: 10.3389/fdgth.2022.799341] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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Sagdic K, Eş I, Sitti M, Inci F. Smart materials: rational design in biosystems via artificial intelligence. Trends in Biotechnology 2022. [DOI: 10.1016/j.tibtech.2022.01.005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
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Alafeef M, Dighe K, Moitra P, Pan D. Monitoring the Viral Transmission of SARS-CoV-2 in Still Waterbodies Using a Lanthanide-Doped Carbon Nanoparticle-Based Sensor Array. ACS Sustain Chem Eng 2022;10:245-58. [PMID: 35036178 DOI: 10.1021/acssuschemeng.1c06066] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
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Diéguez-santana K, Rasulev B, González-díaz H. Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks. Environ Sci : Nano 2022;9:1391-413. [DOI: 10.1039/d1en00967b] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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Jiang Y, Jiang Z, Wang M, Ma L. Current understandings and clinical translation of nanomedicines for breast cancer therapy. Adv Drug Deliv Rev 2022;180:114034. [PMID: 34736986 DOI: 10.1016/j.addr.2021.114034] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
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Smith CW, Hizir MS, Nandu N, Yigit MV. Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling. Anal Chem 2021. [PMID: 34964601 DOI: 10.1021/acs.analchem.1c04379] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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Srivastava I, Moitra P, Fayyaz M, Pandit S, Kampert TL, Fathi P, Alanagh HR, Dighe K, Alafeef M, Vuong K, Jabeen M, Nie S, Irudayaraj J, Pan D. Rational Design of Surface-State Controlled Multicolor Cross-Linked Carbon Dots with Distinct Photoluminescence and Cellular Uptake Properties. ACS Appl Mater Interfaces 2021;13:59747-60. [PMID: 34878252 DOI: 10.1021/acsami.1c19995] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
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Raghushaker CR, Rodrigues J, Nayak SG, Ray S, Urala AS, Satyamoorthy K, Mahato KK. Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study. Anal Chem 2021;93:16520-7. [PMID: 34846862 DOI: 10.1021/acs.analchem.1c03650] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
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Hu Q, Wang S, Duan H, Liu Y. A Fluorescent Biosensor for Sensitive Detection of Salmonella Typhimurium Using Low-Gradient Magnetic Field and Deep Learning via Faster Region-Based Convolutional Neural Network. Biosensors (Basel) 2021;11:447. [PMID: 34821663 DOI: 10.3390/bios11110447] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. Nanoscale 2021;13:17854-70. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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Kemp JA, Kwon YJ. Cancer nanotechnology: current status and perspectives. Nano Converg 2021;8:34. [PMID: 34727233 DOI: 10.1186/s40580-021-00282-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 21] [Article Influence: 7.5] [Reference Citation Analysis]
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He J, Zhang K. Medical image analysis of multiple myeloma based on convolutional neural network. Expert Systems 2022;39. [DOI: 10.1111/exsy.12810] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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Millagaha Gedara NI, Xu X, DeLong R, Aryal S, Jaberi-Douraki M. Global Trends in Cancer Nanotechnology: A Qualitative Scientific Mapping Using Content-Based and Bibliometric Features for Machine Learning Text Classification. Cancers (Basel) 2021;13. [PMID: 34503227 DOI: 10.3390/cancers13174417] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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Ortega-Tenezaca B, González-Díaz H. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. Nanoscale 2021;13:1318-30. [PMID: 33410431 DOI: 10.1039/d0nr07588d] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
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Alafeef M, Dighe K, Moitra P, Pan D. Rapid, Ultrasensitive, and Quantitative Detection of SARS-CoV-2 Using Antisense Oligonucleotides Directed Electrochemical Biosensor Chip. ACS Nano 2020. [PMID: 33079516 DOI: 10.1021/acsnano.0c06392] [Cited by in Crossref: 221] [Cited by in F6Publishing: 237] [Article Influence: 73.7] [Reference Citation Analysis]
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