Impact of Machine Learning on Raman and Raman Optical Activity (ROA) Spectroscopic Analyses of ribonucleic acid structure | ||||
| Egyptian Academic Journal of Biological Sciences. C, Physiology and Molecular Biology | ||||
| Volume 16, Issue 1, June 2024, Page 417-430 PDF (778.4 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/eajbsc.2024.360286 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Omer Azher | ||||
| Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha, Saudi Arabia | ||||
| Abstract | ||||
| The potential of machine learning (ML) to revolutionize analytical sciences, especially, Raman and ROI spectra collection for RNA nucleotides is paramount. ML provides exceptional opportunities for the speedy extraction of vast information from the complex dataset generated by various analytical techniques including spectroscopy which could expedite the determination of the behaviour of complex molecules with the utmost accuracy. Ribonucleic acid (RNA) molecules exist in all living cells. These polymers play significant roles in various biochemical processes, such as translation and protein synthesis. The function of RNA as a catalyst for several cellular reactions in addition to its significant role in gene expression shapes the biological system. The functional versatility of RNAs depends on their ability to fold in various structural conformations, which necessitates delineating the motifs and elements’ structures in RNA to gain a comprehensive insight into the functional versatilities of these biopolymers. Moreover, the pivotal role of these polymers in diagnosis and therapy could be comprehended by functional activity analysis of RNAs using Raman and ROA spectroscopy in conjunction with ML and artificial intelligence. The current review aimed to shed light on the impact of ML algorithms on Raman and ROA spectroscopic RNA structural data analysis. Additionally, this review summarizes the RNA structural organization and methodological approaches of ML-assisted Raman and ROA spectroscopies for RNA in tandem with traditional algorithms. The future directions of the ML-assisted Raman and ROA for RNA structural analysis have also been highlighted to boost biomolecular research efficiency and accuracy. | ||||
| Keywords | ||||
| Artificial Intelligence; RNA; spectroscopy; analysis; structural organization | ||||
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