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For: Lee LC, Liong C, Jemain AA. Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps. Analyst 2018;143:3526-39. [DOI: 10.1039/c8an00599k] [Cited by in Crossref: 143] [Cited by in F6Publishing: 204] [Article Influence: 35.8] [Reference Citation Analysis]
Number Citing Articles
1 Ordoudi SA, Özdikicierler O, Tsimidou MZ. Detection of ternary mixtures of virgin olive oil with canola, hazelnut or safflower oils via non-targeted ATR-FTIR fingerprinting and chemometrics. Food Control 2022;142:109240. [DOI: 10.1016/j.foodcont.2022.109240] [Reference Citation Analysis]
2 Msimanga HZ, Dockery CR, Vandenbos DD. Classification of local diesel fuels and simultaneous prediction of their physicochemical parameters using FTIR-ATR data and chemometrics. Spectrochim Acta A Mol Biomol Spectrosc 2022;279:121451. [PMID: 35675738 DOI: 10.1016/j.saa.2022.121451] [Reference Citation Analysis]
3 Varela JI, Miller ND, Infante V, Kaeppler SM, de Leon N, Spalding EP. A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits. Food Chemistry 2022;391:133264. [DOI: 10.1016/j.foodchem.2022.133264] [Reference Citation Analysis]
4 Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. Pure and Applied Chemistry 2022;0. [DOI: 10.1515/pac-2022-0202] [Reference Citation Analysis]
5 Tew WY, Ying C, Wujun Z, Baocai L, Yoon TL, Yam MF, Jingying C. Application of FT-IR spectroscopy and chemometric technique for the identification of three different parts of Camellia nitidissima and discrimination of its authenticated product. Front Pharmacol 2022;13:931203. [DOI: 10.3389/fphar.2022.931203] [Reference Citation Analysis]
6 Alharbi B, Cozzolino D, Sikulu-Lord M, Whiley D, Trembizki E. Near-infrared spectroscopy as a feasible method for the differentiation of Neisseria gonorrhoeae from Neisseria commensals and antimicrobial resistant from susceptible gonococcal strains. J Microbiol Methods 2022;201:106576. [PMID: 36096277 DOI: 10.1016/j.mimet.2022.106576] [Reference Citation Analysis]
7 Yang Q, Li Y, Li B, Gong Y. A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data. Comput Biol Med 2022;148:105956. [PMID: 35981456 DOI: 10.1016/j.compbiomed.2022.105956] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Kaushal S, Nayi P, Rahadian D, Chen H. Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review. Agriculture 2022;12:1359. [DOI: 10.3390/agriculture12091359] [Reference Citation Analysis]
9 Mansuri SM, Chakraborty SK, Mahanti NK, Pandiselvam R. Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling. Food Control 2022;139:109077. [DOI: 10.1016/j.foodcont.2022.109077] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
10 Khanban F, Bagheri Garmarudi A, Parastar H, Toth G. Evaluation of FT-IR spectroscopy combined with SIMCA and PLS‑DA for detection of adulterants in pistachio butter. Infrared Physics & Technology 2022. [DOI: 10.1016/j.infrared.2022.104369] [Reference Citation Analysis]
11 Hou X, Wang Y, Ke C, Li M, Pan C. Metabolomics and Biomarkers in Retinal and Choroidal Vascular Diseases. Metabolites 2022;12:814. [DOI: 10.3390/metabo12090814] [Reference Citation Analysis]
12 Ji X. Applications of headspace solid-phase microextraction in human biological matrix analysis. Reviews in Analytical Chemistry 2022;41:180-8. [DOI: 10.1515/revac-2022-0042] [Reference Citation Analysis]
13 Calvo-gomez O, Calvo H, Cedillo-barrón L, Vivanco-cid H, Alvarado-orozco JM, Fernandez-benavides DA, Arriaga-pizano L, Ferat-osorio E, Anda-garay JC, López-macias C, López MG. Potential of ATR-FTIR–Chemometrics in Covid-19: Disease Recognition. ACS Omega. [DOI: 10.1021/acsomega.2c01374] [Reference Citation Analysis]
14 Jeng M, Sharma M, Lee C, Lu Y, Tsai C, Chang C, Chen S, Lin R, Chang L. Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury. JCM 2022;11:4829. [DOI: 10.3390/jcm11164829] [Reference Citation Analysis]
15 Al Mehedi Hasan M, Maniruzzaman M, Shin J. Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models. Sci Rep 2022;12:13963. [PMID: 35978028 DOI: 10.1038/s41598-022-18273-x] [Reference Citation Analysis]
16 Zanella L, Facco P, Bezzo F, Cimetta E. Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study. Int J Mol Sci 2022;23:9087. [PMID: 36012350 DOI: 10.3390/ijms23169087] [Reference Citation Analysis]
17 Mancini RSN, Sabaine AE, Castro CE, Carnielli JBT, Dietze R, de Oliveira VL, Lanfredi AJC, Kubota LT, Mamián-lópez MB, Alves WA. Development and Validation of a SERS-Based Serological Test Combined with PLS-DA Method for Leishmaniasis Detection. ACS Appl Electron Mater . [DOI: 10.1021/acsaelm.2c00625] [Reference Citation Analysis]
18 Zhou L, Wang X, Zhang C, Zhao N, Taha MF, He Y, Qiu Z. Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model. Food Bioprocess Technol. [DOI: 10.1007/s11947-022-02866-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Li Y, Bi Q, Wei W, Yao C, Zhang J, Guo D. Sequential decision fusion pipeline for the high-throughput species recognition of medicinal caterpillar fungus by using ATR-FTIR. Microchemical Journal 2022;179:107437. [DOI: 10.1016/j.microc.2022.107437] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Pereira de Souza NM, Machado BH, Padoin LV, Prá D, Fay AP, Corbellini VA, Rieger A. Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer. Talanta 2022. [DOI: 10.1016/j.talanta.2022.123858] [Reference Citation Analysis]
21 Li J, Yao A, Yao J, Zhou J, Zhang J, Wei L, Gong Z, Zhang Z. Dynamic profiles of rose jam metabolomes reveal sugar-pickling impacts on their nutrient content. Food Bioscience 2022. [DOI: 10.1016/j.fbio.2022.101947] [Reference Citation Analysis]
22 Traquete F, Luz J, Cordeiro C, Sousa Silva M, Ferreira AEN. Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics. Front Mol Biosci 2022;9:917911. [DOI: 10.3389/fmolb.2022.917911] [Reference Citation Analysis]
23 Zhang J, Wang K, Chen L. Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/1515810] [Reference Citation Analysis]
24 Surkova A, Bogomolov A, Paderina A, Khistiaeva V, Boichenko E, Grachova E, Kirsanov D. Optical Multisensor System Based on Lanthanide(III) Complexes as Near-Infrared Light Sources for Analysis of Milk. Chemosensors 2022;10:288. [DOI: 10.3390/chemosensors10070288] [Reference Citation Analysis]
25 Spick M, Lewis HM, Frampas CF, Longman K, Costa C, Stewart A, Dunn-Walters D, Greener D, Evetts G, Wilde MJ, Sinclair E, Barran PE, Skene DJ, Bailey MJ. An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics. Sci Rep 2022;12:11867. [PMID: 35831456 DOI: 10.1038/s41598-022-16123-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Sharma A, Sharma V. Forensic analysis of cigarette ash using ATR-FTIR spectroscopy and chemometric methods. Microchemical Journal 2022;178:107406. [DOI: 10.1016/j.microc.2022.107406] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Nkosi NJ, Shoko T, Manhivi VE, Slabbert RM, Sultanbawa Y, Sivakumar D. Metabolomic and chemometric profiles of ten southern African indigenous fruits. Food Chemistry 2022;381:132244. [DOI: 10.1016/j.foodchem.2022.132244] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Kui H, Su H, Wang Q, Liu C, Li Y, Tian Y, Kong J, Sun G, Huang J. Serum metabolomics study of anxiety disorder patients based on LC-MS. Clin Chim Acta 2022:S0009-8981(22)01212-8. [PMID: 35779624 DOI: 10.1016/j.cca.2022.06.022] [Reference Citation Analysis]
29 Deng Y, Luo X, Li X, Xiao Y, Xu B, Tong H. Screening of Biomarkers and Toxicity Mechanisms of Rifampicin-Induced Liver Injury Based on Targeted Bile Acid Metabolomics. Front Pharmacol 2022;13:925509. [PMID: 35754491 DOI: 10.3389/fphar.2022.925509] [Reference Citation Analysis]
30 Dhanani T, Dou T, Biradar K, Jifon J, Kurouski D, Patil BS. Raman Spectroscopy Detects Changes in Carotenoids on the Surface of Watermelon Fruits During Maturation. Front Plant Sci 2022;13:832522. [PMID: 35712570 DOI: 10.3389/fpls.2022.832522] [Reference Citation Analysis]
31 Pochakom A, Mu C, Rho JM, Tompkins TA, Mayengbam S, Shearer J. Selective Probiotic Treatment Positively Modulates the Microbiota-Gut-Brain Axis in the BTBR Mouse Model of Autism. Brain Sci 2022;12:781. [PMID: 35741667 DOI: 10.3390/brainsci12060781] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Xu W, Xia J, Min S, Xiong Y. Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types. Spectrochim Acta A Mol Biomol Spectrosc 2022;274:121034. [PMID: 35248857 DOI: 10.1016/j.saa.2022.121034] [Reference Citation Analysis]
33 Li G, Ma S, Li K, Zhou M, Lin L. Heterogeneity classification based on hyperspectral transmission imaging and multivariate data analysis. Infrared Physics & Technology 2022;123:104180. [DOI: 10.1016/j.infrared.2022.104180] [Reference Citation Analysis]
34 Ye W, Yan T, Zhang C, Duan L, Chen W, Song H, Zhang Y, Xu W, Gao P. Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning. Foods 2022;11:1609. [DOI: 10.3390/foods11111609] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Lasalvia M, Capozzi V, Perna G. A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra. Applied Sciences 2022;12:5345. [DOI: 10.3390/app12115345] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
36 Huang S, Gao Y, Zhang X, Lu J, Wei J, Mei H, Xing J, Pan X. Development of Simple and Accurate in Silico Ligand-Based Models for Predicting ABCG2 Inhibition. Front Chem 2022;10:863146. [DOI: 10.3389/fchem.2022.863146] [Reference Citation Analysis]
37 Farber C, Kurouski D. Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming. Front Plant Sci 2022;13:887511. [PMID: 35557733 DOI: 10.3389/fpls.2022.887511] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Kalogiouri NP, Manousi N, Paraskevopoulou A, Mourtzinos I, Zachariadis GA, Rosenberg E. Headspace Solid-Phase Microextraction Followed by Gas Chromatography-Mass Spectrometry as a Powerful Analytical Tool for the Discrimination of Truffle Species According to Their Volatiles. Front Nutr 2022;9:856250. [PMID: 35558753 DOI: 10.3389/fnut.2022.856250] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Thornley RH, Verhoef A, Gerard FF, White K. The Feasibility of Leaf Reflectance-Based Taxonomic Inventories and Diversity Assessments of Species-Rich Grasslands: A Cross-Seasonal Evaluation Using Waveband Selection. Remote Sensing 2022;14:2310. [DOI: 10.3390/rs14102310] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
40 Yin K, Wang D, Zhao H, Wang Y, Zhang Y, Liu Y, Li B, Xing M. Polystyrene microplastics up-regulates liver glutamine and glutamate synthesis and promotes autophagy-dependent ferroptosis and apoptosis in the cerebellum through the liver-brain axis. Environ Pollut 2022;:119449. [PMID: 35550135 DOI: 10.1016/j.envpol.2022.119449] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
41 Femenias A, Gatius F, Ramos AJ, Teixido-orries I, Marín S. Hyperspectral imaging for the classification of individual cereal kernels according to fungal and mycotoxins contamination: A review. Food Research International 2022;155:111102. [DOI: 10.1016/j.foodres.2022.111102] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
42 Malinick AS, Stuart DD, Lambert AS, Cheng Q. Surface plasmon resonance imaging (SPRi) in combination with machine learning for microarray analysis of multiple sclerosis biomarkers in whole serum. Biosensors and Bioelectronics: X 2022;10:100127. [DOI: 10.1016/j.biosx.2022.100127] [Reference Citation Analysis]
43 Prakash Sharma C, Sharma S, Singh Rawat G, Singh R. Rapid and non-destructive differentiation of Shahtoosh from Pashmina/Cashmere wool using ATR FT-IR spectroscopy. Sci Justice 2022;62:349-57. [PMID: 35598927 DOI: 10.1016/j.scijus.2022.04.002] [Reference Citation Analysis]
44 Wang C, Zhang J, Lv J, Li J, Gao Y, Patience BE, Niu T, Yu J, Xie J. Effect of Methyl Jasmonate Treatment on Primary and Secondary Metabolites and Antioxidant Capacity of the Substrate and Hydroponically Grown Chinese Chives. Front Nutr 2022;9:859035. [PMID: 35449536 DOI: 10.3389/fnut.2022.859035] [Reference Citation Analysis]
45 Havmand PU, Zachariassen LG, Ipsen R, Poulsen VK. Measurement of water-holding capacity in fermented milk using near-infrared spectroscopy combined with chemometric methods. J Dairy Res 2022;:1-7. [PMID: 35388774 DOI: 10.1017/S0022029922000279] [Reference Citation Analysis]
46 Caraballo E, Dare S, Beaudoin G. Variation of trace elements in chalcopyrite from worldwide Ni-Cu sulfide and Reef-type PGE deposits: implications for mineral exploration. Miner Deposita. [DOI: 10.1007/s00126-021-01091-y] [Reference Citation Analysis]
47 Ding M, Fan JL, Huang DF, Jiang Y, Li MN, Zheng YQ, Yang XP, Li P, Yang H. From non-targeted to targeted GC-MS metabolomics strategy for identification of TCM preparations containing natural and artificial musk. Chin Med 2022;17:41. [PMID: 35365201 DOI: 10.1186/s13020-022-00594-8] [Reference Citation Analysis]
48 Li Z, Tian L, Jiang Q, Yan X. Dynamic Nonlinear Process Monitoring based on Dynamic Correlation Variable Selection and Kernel Principal Component Regression. Journal of the Franklin Institute 2022. [DOI: 10.1016/j.jfranklin.2022.04.021] [Reference Citation Analysis]
49 Gocławski J, Korzeniewska E, Sekulska-nalewajko J, Kiełbasa P, Dróżdż T. Method of Biomass Discrimination for Fast Assessment of Calorific Value. Energies 2022;15:2514. [DOI: 10.3390/en15072514] [Reference Citation Analysis]
50 Pérez-cova M, Tauler R, Jaumot J. Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution. Separations 2022;9:79. [DOI: 10.3390/separations9030079] [Reference Citation Analysis]
51 Pavlovic D, Czerkawski M, Davison C, Marko O, Michie C, Atkinson R, Crnojevic V, Andonovic I, Rajovic V, Kvascev G, Tachtatzis C. Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars. Sensors (Basel) 2022;22:2323. [PMID: 35336494 DOI: 10.3390/s22062323] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Corsaro C, Vasi S, Neri F, Mezzasalma AM, Neri G, Fazio E. NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches. Applied Sciences 2022;12:2824. [DOI: 10.3390/app12062824] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
53 Haldavnekar R, Ganesh S, Venkatakrishnan K, Tan B. Cancer Stem Cell DNA Enabled Real-Time Genotyping with Self-Functionalized Quantum Superstructures-Overcoming the Barriers of Noninvasive cfDNA Cancer Diagnostics. Small Methods 2022;:e2101467. [PMID: 35247038 DOI: 10.1002/smtd.202101467] [Reference Citation Analysis]
54 Qiu X, Wang H, Lan Y, Miao J, Pan C, Sun W, Li G, Wang Y, Zhao X, Zhu Z, Zhu S. Blood biomarkers of post-stroke depression after minor stroke at three months in males and females. BMC Psychiatry 2022;22:162. [PMID: 35241021 DOI: 10.1186/s12888-022-03805-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
55 Martín-torres S, Ruiz-castro L, Jiménez-carvelo AM, Cuadros-rodríguez L. Applications of multivariate data analysis in shelf life studies of edible vegetal oils – A review of the few past years. Food Packaging and Shelf Life 2022;31:100790. [DOI: 10.1016/j.fpsl.2021.100790] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
56 Ladisa C, Ma Y, Habibi HR. Metabolic Changes During Growth and Reproductive Phases in the Liver of Female Goldfish (Carassius auratus). Front Cell Dev Biol 2022;10:834688. [DOI: 10.3389/fcell.2022.834688] [Reference Citation Analysis]
57 Amin MO, Al-Hetlani E, Lednev IK. Detection and identification of drug traces in latent fingermarks using Raman spectroscopy. Sci Rep 2022;12:3136. [PMID: 35210525 DOI: 10.1038/s41598-022-07168-6] [Reference Citation Analysis]
58 Tunny SS, Amanah HZ, Faqeerzada MA, Wakholi C, Kim MS, Baek I, Cho BK. Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables. Sensors (Basel) 2022;22:1775. [PMID: 35270921 DOI: 10.3390/s22051775] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Ruiz JJ, Marro M, Galván I, Bernabeu-Wittel J, Conejo-Mir J, Zulueta-Dorado T, Guisado-Gil AB, Loza-Álvarez P. Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis. Cancers (Basel) 2022;14:1056. [PMID: 35205803 DOI: 10.3390/cancers14041056] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Yang Z, Han S, Zhang T, Kusumanchi P, Huda N, Tyler K, Chandler K, Skill NJ, Tu W, Shan M, Jiang Y, Maiers JL, Perez K, Ma J, Liangpunsakul S. Transcriptomic Analysis Reveals the Messenger RNAs Responsible for the Progression of Alcoholic Cirrhosis. Hepatol Commun 2022. [PMID: 35134262 DOI: 10.1002/hep4.1903] [Reference Citation Analysis]
61 Sharma S, Sumesh K, Sirisomboon P. Rapid ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system. Measurement 2022;189:110464. [DOI: 10.1016/j.measurement.2021.110464] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
62 Schütz D, Riedl J, Achten E, Fischer M. Fourier-transform near-infrared spectroscopy as a fast screening tool for the verification of the geographical origin of grain maize (Zea mays L.). Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108892] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
63 Liu J, Kong S, Song S, Dong H, Zhang Z, Fan T. Metabolic Variation Dictates Cardiac Pathogenesis in Patients With Tetralogy of Fallot. Front Pediatr 2022;9:819195. [DOI: 10.3389/fped.2021.819195] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Kehoe ER, Fitzgerald BL, Graham B, Islam MN, Sharma K, Wormser GP, Belisle JT, Kirby MJ. Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease. Sci Rep 2022;12:1478. [PMID: 35087163 DOI: 10.1038/s41598-022-05451-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
65 Wang H, Chen S, Han Z, Li T, Ma J, Chen X, Pang J, Wang Q, Shen Q, Zhang M. Screening of Phospholipids in Plasma of Large-Artery Atherosclerotic and Cardioembolic Stroke Patients With Hydrophilic Interaction Chromatography-Mass Spectrometry. Front Mol Biosci 2022;9:794057. [DOI: 10.3389/fmolb.2022.794057] [Reference Citation Analysis]
66 Zhou N, Liu L, Zou R, Zou M, Zhang M, Cao F, Liu W, Yuan H, Huang G, Ma L, Chen X. Circular Network of Coregulated Sphingolipids Dictates Chronic Hypoxia Damage in Patients With Tetralogy of Fallot. Front Cardiovasc Med 2022;8:780123. [DOI: 10.3389/fcvm.2021.780123] [Reference Citation Analysis]
67 Katsara K, Psatha K, Kenanakis G, Aivaliotis M, Papadakis VM. Subtyping on Live Lymphoma Cell Lines by Raman Spectroscopy. Materials (Basel) 2022;15:546. [PMID: 35057267 DOI: 10.3390/ma15020546] [Reference Citation Analysis]
68 Zhao JJ, Guo XM, Wang XC, Zhang Y, Ma XL, Ma MH, Zhang JN, Liu JN, Yu YJ, Lv Y, She YB. A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics. J Chromatogr A 2022;1664:462801. [PMID: 35007865 DOI: 10.1016/j.chroma.2021.462801] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
69 Yang Q, Xing Q, Yang Q, Gong Y. Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning. Computational and Structural Biotechnology Journal 2022;20:5054-64. [DOI: 10.1016/j.csbj.2022.09.014] [Reference Citation Analysis]
70 Zhou L, Tan L, Zhang C, Zhao N, He Y, Qiu Z. A portable NIR-system for mixture powdery food analysis using deep learning. LWT 2022;153:112456. [DOI: 10.1016/j.lwt.2021.112456] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
71 Weisser J, Pohl T, Heinzinger M, Ivleva NP, Hofmann T, Glas K. The identification of microplastics based on vibrational spectroscopy data – a critical review of data analysis routines. TrAC Trends in Analytical Chemistry 2022. [DOI: 10.1016/j.trac.2022.116535] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
72 Sanaeifar A, Zhu F, Sha J, Li X, He Y, Zhan Z. Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra. Sci Total Environ 2022;802:149824. [PMID: 34454145 DOI: 10.1016/j.scitotenv.2021.149824] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
73 Fernandes DP, Rossetto R, Montenegro AR, Fernandes CCL, Bravo PA, Moreno ME, Cavalcanti CM, Kubota GA, Rondina D. Effectiveness of near-infrared spectroscopy as a non-invasive tool to discriminate spectral profiles of in vitro cultured oocytes from goats. Anim Reprod 2021;18:e20200255. [PMID: 34925556 DOI: 10.1590/1984-3143-AR2020-0255] [Reference Citation Analysis]
74 Yu L, Zeng Z, Tan H, Feng Q, Zhou Q, Hu J, Li Y, Wang J, Yang W, Feng J, Xu B. Significant metabolic alterations in patients with hepatitis B virus replication observed via serum untargeted metabolomics shed new light on hepatitis B virus infection. J Drug Target 2021;:1-8. [PMID: 34844491 DOI: 10.1080/1061186X.2021.2009841] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
75 Lee SM, Kim HU. Development of computational models using omics data for the identification of effective cancer metabolic biomarkers. Mol Omics 2021;17:881-93. [PMID: 34608924 DOI: 10.1039/d1mo00337b] [Reference Citation Analysis]
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