Artificial Intelligence in Metabolomic Research

Öz

The term "metabolomics" refers to high-throughput methods for detecting various metabolites and small molecules in biological samples. Undirected metabolomics, also known as unbiased global metabolome analysis, can be used to discover key metabolites as variables or measurements of human health and illness. From this vantage point, it is investigated how artificial intelligence and machine learning enable significant advances in non-targeted metabolic processes as well as significant findings in the early detection and diagnosis of diseases. Metabolomics is important for finding cures for many diseases. In the development of innovations in the field of biotechnology, it is of great importance to collect, filter, analyse, and use biological information in smart data. For this reason, many biotechnology companies and various healthcare organizations around the world have created large biological databases. This biological data accelerates the development of products in many areas. Algorithms are being developed for biological data analysis. It is thought that many disease treatments will be found when the human genome is edited. Machine learning techniques are effective tools for metabolomic investigation; however, they can only be used in straightforward computing scenarios. When used functionally, data formatting frequently calls for the use of sub-computational resources that are not covered in this area.

Anahtar Kelimeler

• metabolomics
• artificial intelligence
• medicine
• machine learning

Kaynakça

1. Bertini I, Calabro A, De Carli V, Luchinat C, Nepi S, Porfirio B et al (2009). The metabonomic signature of celiac disease. J Proteome Res 8:170–177.
2. Boozari M, Hosseinzadeh H. (2020). Natural products for COVID¬19 prevention and treatment regarding to previous coronavirus infections and novel studies. Phytotherapy Res. p. 1-13
3. Bren L. (2005). Metabolomics: Working toward personalized medicine. FDA Consum, 39: 28-33.
4. Dias-Audibert, F. L., Navarro, L. C., Oliveira, D. N., Melo, C. F. O. R., Guerreiro, T. M., Dabaja, M. Z., et al., (2019). Combining Machine Learning and Metabolomics to IdentifyWeight Gain Biomarkers. Campinas: IEEE Dataport.
5. Giannakos, Michail & Voulgari, Iro & Papavlasopoulou, Sofia & Papamitsiou, Zacharoula & Yannakakis, Georgios. (2020). Games for Artificial Intelligence and Machine Learning Education: Review and Perspectives.
6. Gordon DE, et al. (2020). A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. 583(7816):459–68.
7. Guan W, Zhou M, Hampton CY, Benigno BB, Walker LD, Gray A et al (2009). Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines. BMC Bioinformatics 10:259.
8. Guyon I, Weston J, Barnhill S et al (2002). Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422.

9. Gysi DM, Í Do V, Zitnik M, Ameli A, Gan X, Varol O, Ghiassian SD, Patten JJ, Davey R, Loscalzo J, Barabási AL. (2020). Network medicine framework for identifying drug repurposing opportunities for COVID-19.
10. Heinemann J, Mazurie A, Tokmina-Lukaszewska M, Beilman GJ, Bothner B et al., (2014). Application of support vector machinesto metabolomics experiments with limited replicates. Metabolomics 10:1121–1128.
11. Jacobs DM, Smolders L, Lin Y, de Roo N, Trautwein EA, van Duynhoven J, Mensink RP, Plat J and Mihaleva VV (2017) Effect of Theobromine Consumption on Serum Lipoprotein Profiles in Apparently Healthy Humans with Low HDL-Cholesterol Concentrations. Front. Mol. Biosci. 4:59. doi: 10.3389/fmolb.2017.00059
12. Jang, C., Chen, L., and Rabinowitz, J.D. (2018). Metabolomics and isotope tracing. Cell 173, 822–837.
13. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Funke R. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860-921.
14. Laponogov, I., Gonzalez, G., Shepherd, M., Qureshi, A., Veselkov, D., Charkoftaki, G., ... & Veselkov, K. (2021). Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19. Human genomics, 15(1), 1-11.
15. Lent-Schochet, D., McLaughlin, M., Ramakrishnan, N., and Jialal, I. (2019). Exploratory metabolomics of metabolic syndrome: a status report. World J. Diabetes 10:23.
16. Lin J. M. G., Kourtis S., Ghose, R., Lorente N.P., Kubicek, S., Sdelci, S. (2022). Metabolic Modulation of Transcription: The Role of One-Carbon Metabolism, Cell Chemical Biology, Volume 29, Issue 12, P1664-1679
17. Lin X,Wang Q, Yin P, Tang L, Tan Y, Li., (2011). A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection. Metabolomics 7(4):549–558.
18. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93.
19. Sahu, A., Bla¨ tke, M.A., Szyma nski, J.J., and Topfer, N. (2021). Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput. Struct. Biotechnol. J. 19, 4626–4640.
20. Salman, Ünver S., and Aksan, Kurnaz I. Ed. (2019). Adım Adım Biyogirişimcilik: Biyoteknoloji Girişimci ve Yatırımcılarına Yol Haritası. İstanbul: ABA Yayınları.
21. Smith C, O’Maille G, Want EJ, Qin C, Trauger S, Brandon TR et al (2005). METLIN: a metabolomike mass spectral database. Ther Drug Monit 27(6):747–751
22. Tautenhahn R, Bo¨ttcher C, Neumann S et al (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9:504.
23. Vázquez-Calvo, Á.; De Oya, N.J.; Martín-Acebes, M.A.; Garcia-Moruno, E.; Saiz, J.-C. (2017). Antiviral Properties of the Natural Polyphenols Delphinidin and Epigallocatechin Gallate against the Flaviviruses West Nile Virus, Zika Virus, and Dengue Virus. Front. Microbiol. 8, 1314.
24. VeselKov KA, Vingara LK, Masson P, Robinette SL, Want E, Li JV et al., (2011). Optimizing preprocessing of ultraperformance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83:5864–5872.
25. Xian Y, et al. (2020). Bioactive natural compounds against human coronaviruses: a review and perspective. Acta Pharm Sin B. 10(7):1163–74.
26. Xin Fang, Colton J. Lloyd & Bernhard O. Palsson, (2020). Reconstructing organisms in silico: genome-scale models and their emerging applications, Nature Reviews Microbiology.
27. Yanes O, Tautenhahn R, Patti GJ, Siuzdak G et al (2011). Expanding coverage of the metabolome for global metabolomike profiling. Anal Chem 83(6):2152–2161.
28. Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov RR, Zhebrak A, Minaeva LI,
29. Zagribelnyy BA, Lee LH, Soll R, Madge D, Xing L, Guo T, Aspuru-Guzik A., (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. Sep;37(9):1038-1040.