Abstract
Neurodegenerative diseases affect the central nervous system and lead to death of neuronal cells. Development of therapeutics for neurodegenerative diseases is more challenging as compared to other diseases. Exploring the association of neurodegenerative diseases with human gut microbiome can lead to development of microbiome-based therapeutics. Advancement in tools and techniques has changed the face of traditional clinical genomics. Integration of omics approach with computer-aided drug discovery and artificial intelligence gives a multi-dimensional insight into diagnosis and prognosis of neurodegenerative diseases. Faecal samples of patients of neurodegenerative diseases can be analysed using Next Generation Sequencing approach and can be further integrate with drug discovery approach to get insight into the important genes of highly abundant microbes in human gut. Furthermore, Magnetic Resonance Imaging (MRI), Single-photon emission computed tomography (SPECT), and positron emission tomography (PET) of patients can be analysed using various Artificial Intelligence approach to develop models for prediction and analysis of the neurodegenerative disease as well as classification of diseases.
References
Açıcı, K., Erdaş, Ç. B., Aşuroğlu, T., Toprak, M. K., Erdem, H., & Oğul, H. (2017). A random forest method to detect Parkinson’s disease via gait analysis. In G. Boracchi, L. Iliadis, C. Jayne, & A. Likas (Eds.), Engineering Applications of Neural Networks (Vol. 744, pp. 609–619). Springer International Publishing. https://doi.org/10.1007/978-3-319-65172-9_51.
Agosta, F., Galantucci, S., & Filippi, M. (2017). Advanced magnetic resonance imaging of neurodegenerative diseases. Neurological Sciences, 38(1), 41–51. https://doi.org/10.1007/s10072-016-2764-x
Bäckhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A., & Gordon, J. I. (2005). Host-Bacterial Mutualism in the human intestine. Science, 307(5717), 1915–1920. https://doi.org/10.1126/science.1104816
Bandisode, M. I., Bhanushali, M. R., Singh, M. V., Singh, M. V., & Deshmukh, M. A. (2019). Prediction of Parkinson Disease Using Knn Algorithm, 6(4).
Behjati, S., & Tarpey, P. S. (2013). What is next generation sequencing? Archives of Disease in Childhood—Education & Practice Edition, 98(6), 236–238. https://doi.org/10.1136/archdischild-2013-304340
Bharath, E., & Rajagopalana, T. (2023). Parkinson’s disease classification using random forest kerb feature selection. Intelligent Automation & Soft Computing, 36(2), 1417–1433. https://doi.org/10.32604/iasc.2023.032102
Bhatia, D. A., & Sulekh, R. (2017). Predictive model for Parkinson’s disease through Naïve Bayes Classification, 9(1).
Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82(4), 239–259. https://doi.org/10.1007/BF00308809
Braak, H., R. B, U., Gai, W. P., & Del Tredici, K. (2003). Idiopathic Parkinson’s disease: Possible routes by which vulnerable neuronal types may be subject to neuroinvasion by an unknown pathogen. Journal of Neural Transmission, 110(5), 517–536.https://doi.org/10.1007/s00702-002-0808-2.
Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019). Advancing drug discovery via artificial intelligence. Trends in Pharmacological Sciences, 40(8), 592–604. https://doi.org/10.1016/j.tips.2019.06.004
Collins, F. S., & Fink, L. (1995). The human genome project, 19(3).
Erkkinen, M. G., Kim, M.-O., & Geschwind, M. D. (2018). Clinical neurology and epidemiology of the major neurodegenerative diseases. Cold Spring Harbor Perspectives in Biology, 10(4), a033118. https://doi.org/10.1101/cshperspect.a033118
Gromski, P. S., Granda, J. M., & Cronin, L. (2020). Universal chemical synthesis and discovery with ‘The Chemputer.’ Trends in Chemistry, 2(1), 4–12. https://doi.org/10.1016/j.trechm.2019.07.004
Haller, S., Badoud, S., Nguyen, D., Garibotto, V., Lovblad, K. O., & Burkhard, P. R. (2012). Individual detection of patients with parkinson disease using support vector machine analysis of diffusion tensor imaging data: Initial results. American Journal of Neuroradiology, 33(11), 2123–2128. https://doi.org/10.3174/ajnr.A3126
Hauser, R. A., & Olanow, C. W. (1994). Magnetic resonance imaging of neurodegenerative diseases. Journal of Neuroimaging, 4(3), 146–158. https://doi.org/10.1111/jon199443146
Hou, Y., Dan, X., Babbar, M., Wei, Y., Hasselbalch, S. G., Croteau, D. L., & Bohr, V. A. (2019). Ageing as a risk factor for neurodegenerative disease. Nature Reviews Neurology, 15(10), 565–581. https://doi.org/10.1038/s41582-019-0244-7
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., ŽÃdek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.https://doi.org/10.1038/s41586-021-03819-2
Khorasani, A., & Daliri, M. R. (2014). HMM for classification of Parkinson’s disease based on the raw gait data. Journal of Medical Systems, 38(12), 147. https://doi.org/10.1007/s10916-014-0147-5
Kim, J.-W., Kwon, Y., Yun, J.-S., Heo, J.-H., Eom, G.-M., Tack, G.-R., Lim, T.-H., & Koh, S.-B. (2015). Regression models for the quantification of Parkinsonian bradykinesia. Bio-Medical Materials and Engineering, 26(s1), S2249–S2258. https://doi.org/10.3233/BME-151531
Liu, P., Wu, L., Peng, G., Han, Y., Tang, R., Ge, J., Zhang, L., Jia, L., Yue, S., Zhou, K., Li, L., Luo, B., & Wang, B. (2019). Altered microbiomes distinguish Alzheimer’s disease from amnestic mild cognitive impairment and health in a Chinese cohort. Brain, Behavior, and Immunity, 80, 633–643. https://doi.org/10.1016/j.bbi.2019.05.008
Lloyd-Price, J., Abu-Ali, G., & Huttenhower, C. (2016). The healthy human microbiome. Genome Medicine, 8(1), 51. https://doi.org/10.1186/s13073-016-0307-y
Long, J. M., & Holtzman, D. M. (2019). Alzheimer disease: An update on pathobiology and treatment strategies. Cell, 179(2), 312–339. https://doi.org/10.1016/j.cell.2019.09.001
Lu, F.-M., & Yuan, Z. (2015). PET/SPECT molecular imaging in clinical neuroscience: Recent advances in the investigation of CNS diseases. Quantitative Imaging in Medicine and Surgery, 5(3).
Mantere, T., Kersten, S., & Hoischen, A. (2019). Long-read sequencing emerging in medical genetics. Frontiers in Genetics, 10, 426. https://doi.org/10.3389/fgene.2019.00426
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016). DeepTox: Toxicity prediction using deep learning. Frontiers in Environmental Science, 3. https://doi.org/10.3389/fenvs.2015.00080
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., & Song, X. (2020). Early Detection of Alzheimer’s disease using magnetic resonance imaging: A novel approach combining convolutional neural networks and ensemble learning. Frontiers in Neuroscience, 14, 259. https://doi.org/10.3389/fnins.2020.00259
Pereira, J. C., Caffarena, E. R., & Dos Santos, C. N. (2016). Boosting docking-based virtual screening with deep learning. Journal of Chemical Information and Modeling, 56(12), 2495–2506. https://doi.org/10.1021/acs.jcim.6b00355
Ronaghi, M. (n.d.). Pyrosequencing sheds light on DNA sequencing.
Rumman, M., Tasneem, A. N., Farzana, S., Pavel, M. I., & Alam, Md. A. (2018). Early detection of Parkinson’s disease using image processing and artificial neural network. 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (IcIVPR), 256–261. https://doi.org/10.1109/ICIEV.2018.8641081
Salipante, S. J., Kawashima, T., Rosenthal, C., Hoogestraat, D. R., Cummings, L. A., Sengupta, D. J., Harkins, T. T., Cookson, B. T., & Hoffman, N. G. (2014). Performance comparison of Illumina and Ion torrent next-generation sequencing platforms for 16S rRNA-based bacterial community profiling. Applied and Environmental Microbiology, 80(24), 7583–7591. https://doi.org/10.1128/AEM.02206-14
Sender, R., Fuchs, S., & Milo, R. (2016). Revised estimates for the number of human and bacteria cells in the body. PLOS Biology, 14(8), e1002533. https://doi.org/10.1371/journal.pbio.1002533
Shahid, A. H., & Singh, M. P. (2020). A deep learning approach for prediction of Parkinson’s disease progression. Biomedical Engineering Letters, 10(2), 227–239. https://doi.org/10.1007/s13534-020-00156-7
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Mission AI: The new system technology. Springer International Publishing.https://doi.org/10.1007/978-3-031-21448-6
Shen, T., Yue, Y., He, T., Huang, C., Qu, B., Lv, W., & Lai, H.-Y. (2021). The Association between the gut microbiota and Parkinson’s disease, a meta-analysis. Frontiers in Aging Neuroscience, 13, 636545. https://doi.org/10.3389/fnagi.2021.636545
Simon, S. A., Zhai, J., Nandety, R. S., McCormick, K. P., Zeng, J., Mejia, D., & Meyers, B. C. (2009). Short-read sequencing technologies for transcriptional analyses. Annual Review of Plant Biology, 60(1), 305–333. https://doi.org/10.1146/annurev.arplant.043008.092032
Stork, C., Chen, Y., Å Ãcho, M., & Kirchmair, J. (2019). Hit Dexter 2.0: Machine-learning models for the prediction of frequent hitters. Journal of Chemical Information and Modeling, 59(3), 1030–1043. https://doi.org/10.1021/acs.jcim.8b00677
Wang, C., & Zhang, Y. (2017). Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. Journal of Computational Chemistry, 38(3), 169–177. https://doi.org/10.1002/jcc.24667
Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 60(1), 573–589. https://doi.org/10.1146/annurev-pharmtox-010919-023324
Zhu, M., Liu, X., Ye, Y., Yan, X., Cheng, Y., Zhao, L., Chen, F., & Ling, Z. (2022). Gut microbiota: A novel therapeutic target for Parkinson’s disease. Frontiers in Immunology, 13, 937555. https://doi.org/10.3389/fimmu.2022.937555
Zoetendal, E. G., Akkermans, A. D. L., & De Vos, W. M. (1998). Temperature gradient gel electrophoresis analysis of 16S rRNA from human Fecal samples reveals stable and host-specific communities of active Bacteria. Applied and Environmental Microbiology, 64(10), 3854–3859. https://doi.org/10.1128/AEM.64.10.3854-3859.1998
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sahu, A.K., Sahoo, R., Jena, L., Saluja, S.S., Sahu, P. (2024). Clinical Genomics to Drug Discovery Using Machine Learning for Neurodegenerative Disorders: A Future Perspective. In: Gaur, L., Abraham, A., Ajith, R. (eds) AI and Neuro-Degenerative Diseases. Studies in Computational Intelligence, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-031-53148-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-53148-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53147-7
Online ISBN: 978-3-031-53148-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)