References

Ali, O., Farooq, A., Yang, M., Jin, V. X., Bjørås, M., Wang, J. (2022). abc4pwm: Affinity-based clustering for position weight matrices in applications of DNA sequence analysis. BMC Bioinf. 23, 83. doi: 10.1186/s12859-022-04615-z

Alqaissi, E., Alotaibi, F., Sher Ramzan, M., Algarni, A. (2023). Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases. Ann. Med.. 55, 2304108. doi: 10.1080/07853890.2024.2304108

Altman, N., Krzywinski, M. (2017). Clustering. Nat. Methods. 14, 545–546.

Bao, Y., Medland, N. A., Fairley, C. K., Wu, J., Shang, X., Chow, E. P. F., et al. (2021). Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches. J. Infect. 82, 48–59. doi: 10.1016/j.jinf.2020.11.007

Becht, E., McInnes, L., Healy, J., Dutertre, C. A., Kwok, I. W. H., Ng, L. G., et al. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44. doi: 10.1038/nbt.4314

Benincà, E., Pinto, S., Cazelles, B., Fuentes, S., Shetty, S., Bogaards, J. A., et al. (2023). Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome. Sci. Rep. 13, 8042. doi: 10.1038/s41598-023-34713-8

Bergmeir, C., Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191, 192–213. Doi: 10.1016/j.ins.2011.12.028

Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., et al. (2020). Gut microbiome, big data, and machine learning to promote precision medicine for cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 635–648. doi: 10.1038/s41575-020-0327-3

Campbell, T. W., Wilson, M. P., Roder, H., MaWhinney, S., Georgantas, R. W., 3rd, Maguire, L. K., et al. (2021). Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. Int. J. Med. Inform. 155, 104594.

Cao, J., Xiao, Y., Zhang, M., Huang, L., Wang, Y., Liu, W., et al. (2023). Deep learning of dual plasma fingerprints for high-performance infection classification. Small 19, e2206349. doi: 10.1002/smll.202206349

Chadaga, K., Chakraborty, C., Prabhu, S., Umakanth, S., Bhat, V., Sampathila, N. (2022). Clinical and laboratory approach to diagnose COVID-19 using machine learning. Interdiscip Sci. 14, 452–470. doi: 10.1007/s12539-021-00499-4

Chen, X., Ren, B., Chen, M., Wang, Q., Zhang, L., Yan, G. (2016). NLLSS: Predicting synergistic drug combinations based on semi-supervised learning. PloS Comput. Biol. 12, e1004975. Doi: 10.1371/journal.pcbi.1004975

David, J. A., Zhichao, A., Mark, A., Chris, A., Allcock, A., Brian, A., et al. (2022). A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell 185, 916–938.e58.

Dawkins, J. J., Allegretti, J. R., Gibson, T. E., McClure, E., Delaney, M., Bry, L., et al. (2022). Gut metabolites predict Clostridioides difficile recurrence. Microbiome 10, 87. doi: 10.1186/s40168-022-01284-1

De Fátima Cobre, A., Surek, M., Stremel, D. P., Fachi, M. M., Lobo Borba, H. H., Tonin, F. S., et al. (2022). Diagnosis and prognosis of COVID-19 employing analysis of patients’ plasma and serum via LC-MS and machine learning. Comput. Biol. Med.. 146, 105659.

Delafiori, J., Navarro, L. C., Siciliano, R. F., de Melo, G. C., Busanello, E. N. B., Nicolau, J. C., et al. (2021). COVID-19 automated diagnosis and risk assessment through metabolomics and machine learning. Anal. Chem. 93, 2471–2479. doi: 10.1021/acs.analchem.0c04497

Deo, R. C. (2015). Machine learning in medicine. Circulation 132, 1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593

Essalat, M., Abolhosseini, M., Le, T. H., Moshtaghion, S. M., Kanavi, M. R. (2023). Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis. Vivo confocal microscopy images Sci. Rep. 13, 8953.

Fabian, P., Gaël, V., Alexandre, G., Vincent, M., Bertrand, T., Olivier, G., et al. (2011). Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830.

Feng, T., Noren, D. P., Kulkarni, C., Mariani, S., Zhao, C., Ghosh, E., et al. (2023). Machine learning-based clinical decision support for infection risk prediction. Front. Med. 10.

Fu, C., Zhang, X., Veri, A. O., Iyer, K. R., Lash, E., Xue, A., et al. (2021). Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets. Nat. Commun. 12 (1), 6497.

Gao, A., Kouznetsova, V. L., Tsigelny, I. F. (2022). Machine-learning-based virtual screening to repurpose drugs for the treatment of Candida albicans infection. Mycoses 65, 794–805. doi: 10.1111/myc.13475

Ghosh, S., Sarkar Paria, D., Chatterjee, S. (2022). Comparative study on bacterial population dynamics of foregut, midgut, and hindgut content of Perionyx excavatus (Perrier) isolated from eco-friendly, non-hazardous vermicompost. Appl. Biochem. Biotechnol. 194, 6126–6139. doi: 10.1007/s12010-022-03970-0

Goodswen, S. J., Barratt, J. L. N., Kennedy, P. J., Kaufer, A., Calarco, L., Ellis, J. T. (2021). Machine learning and applications in microbiology. FEMS Microbiol. Rev. 45, fuab015. doi: 10.1093/femsre/fuab015

Habgood-Coote, D., Wilson, C., Shimizu, C., Barendregt, A. M., Philipsen, R., Galassini, R., et al. (2023). Diagnosis of Childhood Febrile Illness Using a Multiclass Blood RNA Molecular Signature. Med 4, 635–654.e5. doi: 10.1016/j.medj.2023.06.007

He, X., Qian, K., Wang, Z., Zeng, S., Li, H., Li, W. V. (2023). scAce: an adaptive embedding and clustering method for single-cell gene expression data. Bioinf. 39 btad546. doi: 10.1093/bioinformatics/btad546

Jolliffe, I. T., Cadima, J. (2016). Principal component analysis: a review and recent developments. Philos. Trans. A Math Phys. Eng. Sci. 374, 20150202. doi: 10.1098/rsta.2015.0202

Kaufman, S., Rosset, S., Perlich, C., Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discovery Data 6, 1–21. doi: 10.1145/2382577.2382579

Kaul, V., Enslin, S., Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointest Endosc 92, 807–812. doi: 10.1016/j.gie.2020.06.040

Shan, W., Li, X., Yao, H., Lin, K. (2021). Convolutional neural network-based virtual screening. Curr. Med. Chem. 28, 2033–2047. doi: 10.2174/0929867327666200526142958

Siddique, M. A. B., Debnath, A., Nath, N. D., Biswash, M. A. R., Tufael (2018). "Advancing Medical Science through Nanobiotechnology: Prospects, Applications, and Future Directions", Journal of Primeasia, 1.1(1),1-7,10163

Tang, Y., Wang, S., Qu, Y., Cui, Z., Zhang, W. (2023). Consistency and adversarial semi-supervised learning for medical image segmentation. Comput. Biol. Med. 161, 107018. doi: 10.1016/j.compbiomed.2023.107018

Topçuoglu, B. D., Lesniak, N. A., Ruffin, M. T., Wiens, J., Schloss, P. D. (2020). A framework for effective application of machine learning to microbiome-based classification problems. mBio 11, e00434–e00420. doi: 10.1128/mBio.00434-20

Tsuyuzaki, K., Sato, H., Sato, K., Nikaido, I. (2020). Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. Genome Biol. 21 (1), 9.

Tufael & Atikur Rahman Sunnay (2022). Transforming Healthcare with Artificial Intelligence: Innovations, Applications, and Future Challenges, Journal of Primeasia, 3(1), 1–6, 9802

Tufael, Atiqur Rahman Sunny et al. (2023). Artificial Intelligence in Addressing Cost, Efficiency, and Access Challenges in Healthcare, 4(1), 1-5, 9798

Yang, Y., Xu, B., Murray, J., Haverstick, J., Chen, X., Tripp, R. A., et al. (2022). Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning. Biosens Bioelectron 217, 114721. doi: 10.1016/j.bios.2022.114721