Open Access
BIO Web Conf.
Volume 75, 2023
The 5th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BioMIC 2023)
Article Number 03001
Number of page(s) 11
Section Biomolecular and Biotechnology
Published online 15 November 2023
  • L. Xue., F. Xiaojin, S. Xiaodong, H. Ningning, H. Fang, L. Yongping. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2019, Frontiers in Aging Neuroscience (2022) [Google Scholar]
  • World Health Organzation. Dementia. Accessed 2 June 2023. URL: (2022) [Google Scholar]
  • S. Xiaojuan, C. Wei-Dong, W. Yan-Dong. β-Amyloid: the key peptide in the pathogenesis of Alzheimer’s disease, Frontiers in Pharmacology 6, 221 (2015) [Google Scholar]
  • T. Elena., G. Michela., V. Vasciaveo, T. Massimo. Oxidative stress and beta amyloid in Alzhemimer’s disease, which comes first: the chicken or the egg?, Antioxidants 10, 9 (2021) [Google Scholar]
  • D. Ture, A. Michael, D. Dennis. The neuropathological diagnosis of Alzheimer’s disease, Molecular Neurodegeneration 14, 32 (2015) [Google Scholar]
  • S. Tomas, A. Marketa, O. Lubomir, C. Lucie., J. Daniel, H. Martina, K. Jiri, C. Jakub. Cholinesterase and prolyl oligopeptidase inhibitory activities of alkaloids from Argemone platyceras (Papaveraceae), Molecules 22, 1181 (2017) [CrossRef] [PubMed] [Google Scholar]
  • B. Buket, U. Duygu, N. Nurlu, K. Gulen, U. Nehir. Chemical profile, acetylcholinesterase, butyrylcholinesterase, and prolyl oligopeptidase inhibitory activity of Gaucium corniculatum subsp. Refractum, Brazilian Journal of Pharmaceutical Sciences 58, (2022) [Google Scholar]
  • G. Marucci, M. Buccioni, D. Ben, C. Lambertucci, R. Volpini, F. Amenta. Efficacy of acetylcholinesterase inhibitors in Alzheimer’s disease, Nuropharmacology, 190, (2021) [Google Scholar]
  • P. Long, P. Quan. Virtual screening strategies in drug discovery: A brief overview, Vietnam Journal of Science and Technology 59, 4 (2021) [Google Scholar]
  • V. Periwal, S. Bassler, S. Andrejev, N. Gabrielli, K. Patil, A. Typas, K. Patil. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs, PLOS Computational Biology 18, 4 (2022) [Google Scholar]
  • I. Fernandez, J. Peters. Machine learning and deep learning in medicine and neuroimaging, Annals of the Child Neurology Society 1, 2 (2023) [Google Scholar]
  • A. Liaw, M. Wiener. Classification and regression by random forest, R News 2, 3 (2002) [Google Scholar]
  • Harrington, P. Machine Learning in Action. Manning Publications Co. (2012) [Google Scholar]
  • C. Lin, R. Weng, S. Keerthi. Trust Region Newton Method for Large-Scale Logistic Regression, Journal of Machine Learning Research. (2008) [Google Scholar]
  • A. Ademosun, G. Oboh, O. Ajeigbe. Influence of Moringa (Moringa oleifera) enriched ice creams on rats’ brain: Exploring the redox and cholinergic systems, Current Research in Food Science 5, (2002) [Google Scholar]
  • R. Arcusa, D. Villano, J. Marhuenda, M. Cano, B. Cerda, P. Zafrilla. Potential Role of Ginger (Zingiber officinale Roscoe) in the Prevention of Neurodegenerative Diseases, Front. Nutr. 9, (2022) [CrossRef] [Google Scholar]
  • P. Tedeschi, M. Nigro, A. Travagli, M. Catani, A. Cavazzini, S. Merighi, S. Gessi. Therapeutic Potential of Allicin and Aged Garlic Extract in Alzheimer’s Disease, Int. J. Mol. Sci. 23, 6950 (2022) [CrossRef] [Google Scholar]
  • M. Barbosa, A. Justino, M. Martins, K. Belaz, F. Ferreira, R. de Oliveira, A. Danuello, F. Espindola, M. Pivatto. Cholinesterase inhibitors assessment of aporphine alkaloids from Annona crassiflora and molecular docking studies, Bioorganic Chemistry 120, (2022) [Google Scholar]
  • P. Shayan, A. Amir. Evaluation of antioxidant and inhibitory properties of Citrus aurantium L. on the acetylcholinesterase activity and the production of amyloid nano–bio fibrils, International Journal of Biological Macromolecules 182, (2021) [Google Scholar]
  • W. Kim, Y. Kim, E. Cho, E. Byun, W. Park, H. Song, K. Kim, S. Park, E. Byun. Neuroprotective effect of Annona muricata-derived polysaccharides in neuronal HT22 cell damage induced by hydrogen peroxide, Bioscience, Biotechnology, and Biochemistry 84, (2020) [Google Scholar]
  • D. Chicco, G, Jurman. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics. (2020) [Google Scholar]
  • J. Davis, M. Goadrich. The relationship between precision-recall and ROC curves. (2006) [Google Scholar]
  • P. Ananat, P. Gupta. Application of machine learning in understanding bioactivity of betalactamase AmpC, Journal of Physics: Conference Series 2273, (2022) [Google Scholar]
  • K. Gajowniczek, T. Zabkowski. Estimating the ROC curve and its significance for classification models’ assessment, Quantitative Methods in Economics 15, 2 (2014) [Google Scholar]
  • N. Obuchowski. Fundamental of Clinical Research for Radiologists, American Journal of Roentgenology 184, (2005) [Google Scholar]
  • T. Mahesh, D. Kumar, V. Kumar, J. Asghar, B. Bazezew, R. Natarajan, V. Vivek. Blended ensemble learning prediction model for strengthening diagnosis and treatment of chronic diabetes disease, Computational Intelligence and Neuroscience (2022) [Google Scholar]
  • T. Hou. J. Wang, Y. Li ADME evaluation in drug discovery: the prediction of human intestinal absorption by a support vector machine, J. Chem. Inf. Model 47, 6 (2007) [Google Scholar]
  • D. Sen, K. Nandi, D. Saha. Rule of five: The five men army to cross the blood brain barrier for therapeutically potent, World Journal of Advance Healthcare Research 5, 3 (2021) [Google Scholar]
  • M. Pollastri Overview on the rule of five, Current Protocols in Pharmacology 49, (2010) [Google Scholar]
  • T. Altamash, A. Amhamed, S. Aparicio, M. Atilhan. Effect of hydrogen bond donors and acceptors on CO2 absorption by deep eutectic solvents, Processes 8, (2020) [Google Scholar]
  • M. Basanagouda, J. Jadhav, M. Kulkarni, R. Rao. Computer Aided Prediction of Biological Activity Spectra: Study of Correlation between Predicted and Observed Activities for Coumarin-4-Acetic Acids, Indian J Pharm Sci. 73, 1 (2011) [CrossRef] [PubMed] [Google Scholar]
  • R. Cacabelos, R. Llovo, C. Frail, L. Fernández Novoa L. Pharmacogenetic aspects of therapy with cholinesterase inhibitors: the role of CYP2D6 in Alzheimer’s disease pharmacogenetics, Curr Alzheimer Res. 4, 4 (2007) [Google Scholar]
  • S. Ruangritchankul, P. Chantharit, S. Srisuma, L. Gray. Adverse Drug Reactions of Acetylcholinesterase Inhibitors in Older People Living with Dementia: A Comprehensive Literature Review, Ther Clin Risk Manag 17, (2021) [Google Scholar]
  • J. Hakkola, J. Hukkanen, M. Turpeinen, O. Pelkonen. Inhibition and induction of CYP enzymes in humans: an update, Springer Science and Business Media Deutschland GmbH 94, (2020) [Google Scholar]

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