Infrared Spectroscopy‑Enabled Molecular Fingerprinting of Blood Samples Coupled with Machine Learning for Multiplex Disease Screening
Downloads
Molecular fingerprinting of blood samples, when effectively combined with advanced machine learning techniques, facilitates an innovative approach to label-free multiplex screening of a wide array of both acute and chronic diseases. Blood, as a readily accessible and abundant source of vital biochemical information, serves as an exceptionally ideal medium for implementing such comprehensive screening methods. The seamless integration of molecular fingerprinting and artificial intelligence capitalizes on intrinsic molecular signatures, thereby enabling the rapid assessment of disease presence. This supports extensive screening initiatives that are crucial in today's healthcare landscape. Moreover, this multifaceted approach transforms complex raw spectral data into clear and quantifiable molecular profiles. These profiles not only enhance the understanding of various pathological conditions but also provide critical insights into diseases such as cardiovascular conditions, type-2 diabetes mellitus, a range of liver diseases, and different types of cancers. Importantly, this method effectively meets the pressing demand for scalable, cost-effective, and minimally invasive disease screening solutions, especially during resource-constrained scenarios, which may include pandemic outbreaks and public health emergencies. Overall, the fusion of molecular fingerprinting with machine learning holds immense potential to revolutionize disease detection and monitoring practices in the future.
1. W. Kung Peng, T. T. Ng, and T. Ping Loh, "Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy," 2020. ncbi.nlm.nih.gov
2. L. Liu, X. Chen, O. Olayemi Petinrin, W. Zhang et al., "Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey," 2021. ncbi.nlm.nih.gov
3. L. Mathot, E. Falk-Sörqvist, L. Moens, M. Allen et al., "Automated Genotyping of Biobank Samples by Multiplex Amplification of Insertion/Deletion Polymorphisms," 2012. ncbi.nlm.nih.gov
4. Y. Hu, "Molecular Techniques for Blood and Blood Product Screening," 2018. ncbi.nlm.nih.gov
5. H. Skutkova, M. Vitek, M. Bezdicek, E. Brhelova et al., "Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data," 2019. ncbi.nlm.nih.gov
6. T. M. Ghazal, H. Al Hamadi, M. Umar Nasir, undefined Atta-ur-Rahman et al., "Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction," 2022. ncbi.nlm.nih.gov
7. O. Tutsoy and G. Gul Koç, "Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification," 2024. ncbi.nlm.nih.gov
8. Z. Yaari, Y. Yang, E. Apfelbaum, C. Cupo et al., "A perception-based nanosensor platform to detect cancer biomarkers," 2021. ncbi.nlm.nih.gov
9. D. L Tong, D. J Boocock, C. Coveney, J. Saif et al., "A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis:stem cell and melanoma cancer studies," 2011. [PDF]
10. D. L Tong, D. J Boocock, C. Coveney, J. Saif et al., "A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies," 2011. ncbi.nlm.nih.gov
11. L. Zhang, Q. Liu, Y. Guo, L. Tian et al., "DNA-based molecular classifiers for the profiling of gene expression signatures," 2024. ncbi.nlm.nih.gov
12. H. Salamon, M. R Segal, A. Ponce de Leon, and P. M Small, "Accommodating error analysis in comparison and clustering of molecular fingerprints.," 1998. ncbi.nlm.nih.gov
13. D. Ortiz Velez, H. Mack, J. Jupe, S. Hawker et al., "Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling," 2017. ncbi.nlm.nih.gov
14. B. T Luke and J. R Collins, "Examining the significance of fingerprint-based classifiers," 2008. ncbi.nlm.nih.gov
15. T. Beduk, D. Beduk, M. Rahil Hasan, E. Guler Celik et al., "Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring," 2022. ncbi.nlm.nih.gov
16. N. Scudder, D. McNevin, S. F. Kelty, S. J. Walsh et al., "Forensic DNA phenotyping: Developing a model privacy impact assessment," 2018. [PDF]
17. M. Robinson and G. Glusman, "Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications.," 2018. [PDF]
18. P. Kumar Dabla, "Unlocking new potential of clinical diagnosis with artificial intelligence: Finding new patterns of clinical and lab data," 2024. ncbi.nlm.nih.gov
19. H. Bangalore, "Leveraging DNA Fingerprinting to Combat Sample Contamination in High-throughput Molecular Testing," 2023. researchgate.net
20. M. Huber, K. V. Kepesidis, L. Voronina, M. Božić, et al., "Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring," *Nature*, vol. 2021. nature.com
21. W. DeGroat, H. Abdelhalim, K. Patel, D. Mendhe et al., "Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine," 2024. ncbi.nlm.nih.gov
22. Z. Ahmed, "Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis," 2020. ncbi.nlm.nih.gov
