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Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction

Chandu Vaidya *, Sajan Rangari **, Tanmay Walke ***, Tanmay Phadnis ****, Yash Deshmukh *****, Yash Kunte ******

*-****** Department of Computer Science Engineering, Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India.

Periodicity:September - November'2020
DOI : https://doi.org/10.26634/jcom.8.3.18048

Abstract

While ageing has a major impact on human health and the economy, little is known about its molecular basis: control and mechanism. More than 300 genes have been linked to human ageing to date (almost all of them function as protein-coding genes). Individual genes or small subsets of these genes linked to ageing have been extensively studied, but overall study of these genes has been restricted. To fill this gap, we looked at different applications of modern artificial intelligence (AI) algorithms in the field of ageing science. Biological Age (BA), also known as the hypothetical underlying age of an organism, has been calculated using a number of machine learning algorithms. Different circulating and non-circulating biomarkers can be used to calculate BA. Based on our study, we expect modern machine learning (ML) models to contribute to the reputation and prominence of healthcare and pharmaceutical longevity biotechnology, as well as the convergence of numerous fields of research. In this paper, we used a variety of machine learning algorithms on the skin fibroblasts cells dataset because it is ideal for age prediction studies for several reasons. These skin fibroblasts contain age-related disruption, contain age-dependent phenotypic, epigenomic, and transcriptomic changes, and it is easy to collect using non-invasive techniques. By using various machine learning models like elastic net, random forest regressor, support vector regressor and artificial neural network for calculating the accuracy of these models on the dataset, we obtain an accuracy of 72 percent with elastic net.

Keywords

Artificial Intelligence, Biological Age, Machine Learning, Accuracy, Dataset.

How to Cite this Article?

Vaidya, C., Rangari, S., Walke, T., Phadnis, T., Deshmukh, Y., and Kunte, Y. (2020). Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction. i-manager's Journal on Computer Science, 8(3), 1-11. https://doi.org/10.26634/jcom.8.3.18048

References

[1]. Andreassen, S. N., Ben Ezra, M., & Scheibye-Knudsen, M. (2019). A defined human aging phenome. Aging, 11(15), 5786-5806. https://doi.org/10.18632/aging.102166

[2]. Ashapkin, V. V., Kutueva, L. I., Kurchashova, S. Y., & Kireev, I. I. (2019). Are there common mechanisms between the Hutchinson–Gilford Progeria Syndrome and natural aging? Frontiers in Genetics, 10, 455. https://doi. org/10.3389/fgene.2019.00455

[3]. Bekaert, S., De Meyer, T., & Van Oostveldt, P. (2005). Telomere attrition as ageing biomarker. Anticancer Research, 25(4), 3011-3021.

[4]. Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., Videira, P. A., & Valadão, G. (2019). Artificial intelligence (AI) in rare diseases: Is the future brighter? Genes, 10(12), 978-1001. https://doi.org/10.3390/genes10120978

[5]. Brehme, M., Voisine, C., Rolland, T., Wachi, S., Soper, J. H., Zhu, Y., ..., & Morimoto, R. I. (2014). A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Reports, 9(3), 1135-1150. https://doi.org/10.1016/j.celrep.2014.09.042

[6]. Campbell, J. M., Mahbub, S., Habibalahi, A., Paton, S., Gronthos, S., & Goldys, E. (2020). Ageing human bone marrow mesenchymal stem cells have depleted NAD (P) H and distinct multispectral autofluorescence. GeroScience, 5(3), 1-10. https://doi.org/10.1007/s11357-020-00250-9

[7]. Childs, B. G., Durik, M., Baker, D. J., & Van Deursen, J. M. (2015). Cellular senescence in aging and age-related disease: From mechanisms to therapy. Nature Medicine, 21(12), 1424-1435. https://doi.org/10.1038/nm.4000

[8]. de Lucia, C., Murphy, T., Steves, C. J., Dobson, R. J., Proitsi, P., & Thuret, S. (2020). Lifestyle mediates the role of nutrient-sensing pathways in cognitive aging: Cellular and epidemiological evidence. Communications Biology, 3(1), 1-17. https://doi.org/10.1038/s42003-020-0844-1

[9]. Fabris, F., De Magalhães, J. P., & Freitas, A. A. (2017). A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188. https://doi.org/ 10.1007/s10522-017-9683-y

[10]. Fleischer, J. G., Schulte, R., Tsai, H. H., Tyagi, S., Ibarra, A., Shokhirev, M. N., ..., & Navlakha, S. (2018). Predicting age from the transcriptome of human dermal fibroblasts. Genome Biology, 19(1), 1-8. https://doi.org/10.1186/s1305 9-018-1599-6

[11]. Garitaonandia, I., Amir, H., Boscolo, F. S., Wambua, G. K., Schultheisz, H. L., Sabatini, K., ..., & Laurent, L. C. (2015). Increased risk of genetic and epigenetic instability in human embryonic stem cells associated with specific culture conditions. PloS One, 10(2). https://doi.org/10.1371/ journal.pone.011830

[12]. Gialluisi, A., Di Castelnuovo, A., Donati, M. B., De Gaetano, G., Iacoviello, L., & Moli-sani Study Investigators. (2019). Machine learning approaches for the estimation of biological aging: The road ahead for population studies. Frontiers in Medicine, 6, 146-153. https://doi.org/10.3389/fmed.2019.00146

[13]. Holder, L. B., Haque, M. M., & Skinner, M. K. (2017). Machine learning for epigenetics and future medical applications. Epigenetics, 12(7), 505-514. https://doi.org/ 10.1080/15592294.2017.1329068

[14]. Jin, B., Li, Y., & Robertson, K. D. (2011). DNA methylation: Superior or subordinate in the epigenetic hierarchy? Genes & Cancer, 2(6), 607-617. https://doi.org/ 10.1177%2F1947601910393957

[15]. Jónsson, B. A., Bjornsdottir, G., Thorgeirsson, T. E., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D. F., ..., & Ulfarsson, M. O. (2019). Brain age prediction using deep learning uncovers associated sequence variants. Nature Communications, 10(1), 1-10. https://doi.org/10.1038/s4 1467-019-13163-9

[16]. Naue, J., Hoefsloot, H. C., Mook, O. R., Rijlaarsdam- Hoekstra, L., van der Zwalm, M. C., Henneman, P., ..., & Verschure, P. J. (2017). Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression. Forensic Science International: Genetics, 31, 19-28. https://doi.org/10.1016 /j.fsigen.2017.07.015

[17]. Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., ..., & Fedichev, P. O. (2018). Extracting biological age from biomedical data via deep learning: Too much of a good thing? Scientific Reports, 8(1), 1-11. https://doi.org/10.1038/s41598-018-23534-9

[18]. Rahman, S. A. (2019). Quantifying human biological age: A machine learning approach. [Doctoral dissertation]. West Virginia University, Morgantown, WV.

[19]. Rebelo-Marques, A., De Sousa Lages, A., Andrade, R., Ribeiro, C. F., Mota-Pinto, A., Carrilho, F., & Espregueira- Mendes, J. (2018). Aging hallmarks: The benefits of physical exercise. Frontiers in Endocrinology, 9, 258. https:// doi.org/10.3389/fendo.2018.00258

[20]. Scheibye-Knudsen, M., Scheibye-Alsing, K., Canugovi, C., Croteau, D. L., & Bohr, V. A. (2013). A novel diagnostic tool reveals mitochondrial pathology in human diseases and aging. Aging, 5(3), 192-208. https://doi.org/ 10.18632/aging.100546

[21]. Schulz, R., Wahl, H. W., Matthews, J. T., De Vito Dabbs, A., Beach, S. R., & Czaja, S. J. (2015). Advancing the aging and technology agenda in gerontology. The Gerontologist, 55(5), 724-734. https://doi.org/10.1093/geront/gnu071

[22]. Tang, W., Chen, J., & Hong, H. (2021). Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods. Chemosphere, 273, 128567. https://doi.org/10. 1016/j.chemosphere.2020.128567

[23]. Tchkonia, T., & Kirkland, J. L. (2018). Aging, cell senescence, and chronic disease: Emerging therapeutic strategies. Journal of American Medical Association, 320(13), 1319-1320. https://doi.org/10.1001/jama.2018.12440

[24]. Trovato-Salinaro, A., Trovato-Salinaro, E., Failla, M., Mastruzzo, C., Tomaselli, V., Gili, E., ..., & Vancheri, C. (2006). Altered intercellular communication in lung fibroblast cultures from patients with idiopathic pulmonary fibrosis. Respiratory Research, 7(1), 1-9. https://doi.org/10. 1186/1465-9921-7-122

[25]. Viñuela, A., Brown, A. A., Buil, A., Tsai, P. C., Davies, M. N., Bell, J. T., ..., & Small, K. S. (2018). Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. Human Molecular Genetics, 27(4), 732-741. https://doi.org/10.1093/hmg/ddx424

[26]. Wood, T. R., Kelly, C., Roberts, M., & Walsh, B. (2019). An interpretable machine learning model of biological age. Research, 8(17), 17-23. https://doi.org/10.12688/f10 00research.17555.1

[27]. Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019). Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Research Reviews, 49, 49-66. https://doi.org/10.1016/j.arr.2018.11.003

[28]. Zouboulis, C. C., & Makrantonaki, E. (2019). Clinical and laboratory skin biomarkers of organ-specific diseases. Mechanisms of Ageing and Development, 177, 144-149. https://doi.org/10.1016/j.mad.2018.08.003

Comparative Analysis of Various Machine Learning Algorithms for Biological Age Prediction

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