Computational Molecular Biology 2024, Vol.14, No.4, 163-172 http://bioscipublisher.com/index.php/cmb 171 Imoto Y., Nakamura T., Escolar E.G., Yoshiwaki M., Kojima Y., Yabuta Y., Katou Y., Yamamoto T., Hiraoka Y., and Saitou M., 2022, Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis, Life Science Alliance, 5(12). https://doi.org/10.26508/lsa.202201591 Jiang X.Y., Kong X.Y., and Ge Z.Q., 2023, Augmented industrial data-driven modeling under the curse of dimensionality, IEEE/CAA Journal of Automatica Sinica, 10(6): 1445-1461. https://doi.org/10.1109/JAS.2023.123396 Jo T., Nho K., and Saykin A.J., 2019, Deep learning in alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data, Frontiers in Aging Neuroscience, 11: 220. https://doi.org/10.3389/fnagi.2019.00220 Juan H.F., and Huang H.C., 2023, Quantitative analysis of high‐throughput biological data, Wiley Interdisciplinary Reviews: Computational Molecular Science, 13(4): e1658. https://doi.org/10.1002/wcms.1658 Kalina J., 2014, Classification methods for high-dimensional genetic data, Biocybernetics and Biomedical Engineering, 34: 10-18. https://doi.org/10.1016/J.BBE.2013.09.007 Karim M.R., Beyan O., Zappa A., Costa I.G., Rebholz-Schuhmann D., Cochez M., and Decker S., 2020, Deep learning-based clustering approaches for bioinformatics, Briefings in Bioinformatics, 22(1): 393-415. https://doi.org/10.1093/bib/bbz170 Kaur P., Singh A., and Chana I., 2021, Computational techniques and tools for omics data analysis: state-of-the-art challenges and future directions, Archives of Computational Methods in Engineering, 28: 4595-4631. https://doi.org/10.1007/s11831-021-09547-0 Kim Y.S., Hao J., Mallavarapu T., Park J., and Kang M., 2019, Hi-lasso: high-dimensional lasso, IEEE Access, 7: 44562-44573. https://doi.org/10.1109/ACCESS.2019.2909071 Leonavicius K., Nainys J., Kučiauskas D., and Mazutis L., 2019, Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches, Current Opinion In Biotechnology, 55: 159-166. https://doi.org/10.1016/j.copbio.2018.09.012 Ma S., and Dai Y., 2011, Principal component analysis based methods in bioinformatics studies, Briefings in bioinformatics, 12(6): 714-722. https://doi.org/10.1093/bib/bbq090 Mirza B., Wang W., Wang J., Choi H., Chung N.C., and Ping P.P., 2019, Machine learning and integrative analysis of biomedical big data, Genes, 10(2): 87. https://doi.org/10.3390/genes10020087 Moon K.R., Dijk D., Wang Z., Gigante S., Burkhardt D.B., Chen W.S., Yim K., Elzen A., Hirn M.J., Coifman R.R., Ivanova N.B., Wolf G., and Krishnaswamy S., 2019, Visualizing structure and transitions in high-dimensional biological data, Nature Biotechnology, 37(12): 1482-1492. https://doi.org/10.1038/s41587-019-0336-3 Palit S., Heuser C., Almeida G.P., Theis F.J., and Zielinski C., 2019, Meeting the challenges of high-dimensional single-cell data analysis in immunology, Frontiers in Immunology, 10: 1515. https://doi.org/10.3389/fimmu.2019.01515 Peralta D., and Saeys Y., 2020, Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data, Appl.Soft Comput, 93: 106421. https://doi.org/10.1016/j.asoc.2020.106421 Ramanathan A., Chennubhotla C.S., Agarwal P.K., and Stanley C.B., 2015, Large-scale machine learning approaches for molecular biophysics, Biophysical Journal, 108(2): 370a. https://doi.org/10.1016/J.BPJ.2014.11.2027 Rocha A., Groen T., Skidmore A., Darvishzadeh R., and Willemen L., 2017, The Naïve Overfitting Index Selection (NOIS): a new method to optimize model complexity for hyperspectral data, Isprs Journal of Photogrammetry and Remote Sensing, 133: 61-74. https://doi.org/10.1016/J.ISPRSJPRS.2017.09.012 ShyamMohanJ., S., 2016, Data reduction techniques for high dimensional biological data, International Journal of Research in Engineering and Technology, 05: 319-324. https://doi.org/10.15623/IJRET.2016.0502058 Song X.F., Zhang Y., Guo Y.N., Sun X.Y., and Wang Y.L., 2020, Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data, IEEE Transactions on Evolutionary Computation, 24(5): 882-895. https://doi.org/10.1109/TEVC.2020.2968743 Viegas F., Rocha L., Gonçalves M., Mourão F., Sá G., Salles T., Andrade G., and Sandin I., 2018, A genetic programming approach for feature selection in highly dimensional skewed data, Neurocomputing, 273: 554-569. https://doi.org/10.1016/j.neucom.2017.08.050 Vinga S., 2020, Structured sparsity regularization for analyzing high-dimensional omics data, Briefings in bioinformatics, 22(1): 77-87. https://doi.org/10.1093/bib/bbaa122 Wörheide M., Krumsiek J., Kastenmüller G., and Arnold M., 2021, Multi-omics integration in biomedical research-A metabolomics-centric review, Analytica Chimica Acta, 1141: 144-162. https://doi.org/10.1016/j.aca.2020.10.038
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