Abstract
Deep Learning is a branch of Machine Learning, which focuses on a set of algorithms that model high-level abstractions in data by using a deep representation of multiple processing layers. The goal of Machine Learning is to map input patterns to output values. This paper will suggest a potential application of Deep Learning Algorithms for the analysis of large amounts of data produced by the research of the Human Microbiome. Humans have coevolved with microbes in the environment, and each body habitat has a unique set of microorganisms (microbiota). The most abundant and well-studied microbiota are found in the gut, where the bacterial density reaches 1011–1012 cells/g in the distal human colon. The number of bacteria in the human gut has been estimated to exceed the number of somatic cells in the body by an order of magnitude and that the biomass of the gut microbiota may reach up to 1.5 kg. This paper presents different methods that have been implemented and tested on a Human Microbiome Dataset. Besides the findings concerning accuracy and runtime, the results suggest that the Deep Learning algorithms could be successfully used to analyze large amounts of Microbiota data.
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References
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Nets 61, 85–117 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Bhattacherjee, A., Sourav, R., Sneha, P., Pazel, R., Noreen, K., Nilanjan, D.: Classification approach for breast cancer detection using back propagation neural network: a study. In: Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, p. 12 (2016). doi:10.4018/978-1-4666-8811-7
Hore, S., et al.: Real time dactylology based selective image encryption using speeded up robust features extraction technique and artificial neural network. In: Image Feature Detectors Foundations, Innovations, and Applications. Studies in Computational Intelligence series (2015)
Samanta, S., et al.: Haralick features based automated glaucoma classification using back propagation neural network. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), vol. 327 (2014)
Geman, O.: Nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier for automatic assessing of tremor severity. In: E-Health and Bioengineering Conference (EHB), pp. 1–4 (2013)
Geman, O., Costin, H.: Automatic assessing of tremor severity using nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier. Adv. Electr. Comput. Eng. 14(1), 133–138 (2014)
Geman, O., Sanei, S., Chiuchisan, I., Graur, A., Prochazka, A., Vysata, O.: Towards an inclusive Parkinson’s screening system. In: Proceedings of the International Conference on System Theory, Control and Computing, pp. 476–480 (2014)
Human Microbiome Project (2016). https://commonfund.nih.gov/hmp/overview
Samuel, B.S., Shaito, A., Motoike, T., Rey, F.E., Backhed, F., Manchester, J.K., Hammer, R.E., Williams, S.C., Crowley, J., Yanagisawa, M., Gordon, J.I.: Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41. Proc. Nat. Acad. Sci. 105, 16767–16772 (2008)
Swartz, T.D., Duca, F.A., De Wouters, T., Sakar, Y., Covasa, M.: Upregulation of intestinal type 1 taste receptor 3 and sodium glucose luminal transporter-1 expression and increased sucrose intake in mice lacking gut microbiota. Br. J. Nutr. 107, 621–630 (2012)
Duca, F.A., Covasa, M.: Current and emerging concepts on the role of peripheral signals in the control of food intake and development of obesity. Br. J. Nutr., 1–16 (2012)
Fettweis, J.M., Brooks, J.P., Serrano, M.G., Sheth, N.U., Vladimir, J.: Microbiology (Reading, England) (2014)
Eren, A., Murat, A., Borisy, G.G., Huse, S.M., Mark Welch, J.: Oligotyping analysis of the human oral microbiome. In: Proceedings of the National Academy of Sciences of the United States of America (2014)
Ma, L., Jungwoo, K., Hatzenpichler, R., Karymov, M.A., Hubert, N., Hanan, I.M., Chang, E.B., Ismagilov, R.F.: Gene-targeted microfluidic cultivation validated by isolation of a gut bacterium listed in Human Microbiome Project’s Most Wanted taxa. In: Proceedings of the National Academy of Sciences of the United States of America, pp. 9768–9773 (2014)
Rooks, M.G., Veiga, P., Wardwell-Scott, L.H., Tickle, T., Segata, N., Michaud, M., Gallini, C.A., Beal, C., Van Hylckama-Vlieg, J.E.T., Ballal, S.A., Morgan, X.C., Glickman, J.N., Huttenhow, D.G.: Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 1403–1417 (2014)
Spinler, J.F., Sontakke, A., Hollister, E.B., Venable, S.F., Phaik, L., Balderas, M.A., Saulnier, D.M.A., Mistretta, T.A., Devaraj, S., Walter, J., Versalovic, J., Highlander, S.K.: From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic functions. Genome Biol. Evol. 1772–1789 (2014)
Ma, L., Datta, S.S., Karymov, M.A., Pan, Q., Begolo, S., Ismagilov, R.F.: Individually addressable arrays of replica microbial cultures enabled by splitting SlipChips. Integr. Biol.: Quant. Biosci. Nano Macro (2014)
Huttenhower, C., Kostic, A.D., Xavier, R.: Inflammatory bowel disease as a model for translating the microbiome. Immunity, 843–854 (2014)
Franzosa, E.A., Xuan Zhang, X.C.: The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. (2015)
D’Argenio, V., Salvatore, F.: The role of the gut microbiome in the healthy adult status. Clin. Chim. Acta (2015)
Mandal, S., et al.: Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 1–7 (2015)
He, C., Shan, Y., Song, W.: Targeting gut microbiota as a possible therapy. Nutr. Res. 35, 361–367 (2015)
Le Barz, M., Anhe, F.E., Varin, T., et al.: Probiotics as complementary treatment for metabolic disorders. Diab. Metab. J. 39, 291–303 (2015)
Wu, Y., Ding, Y., Tanaka, Y.: Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int. J. Med. Sci. 11, 1185–1200 (2014)
Tremaroli, V., Backhed, F.: Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012)
Everard, A., Cani, P.: Diabetes, obesity and gut microbiota. Best Pract. Res. Clin. Gastroenerol. 27, 73–83 (2013)
Apostolescu, C., Moroti, R., Molagic, V., et al.: Gut microbiota and its complex role, the experience of the National Institute for Infectious Diseases “Prof. Dr. Matei Balş” in fecal bacteriotherapy for Clostridium difficile infection. BMC Infect. Dis. 13(Suppl. 1), O19 (2013)
Geman, O., Chiuchisan, I., Covasa, M.: Data mining and knowledge discovery tools for human microbiome big data. In: Proceedings of IEEE - International Conference on Computers Communications and Control, Oradea, Romania, 10–14 May (2016)
Geman, O.: Data mining tools used in deep brain stimulation – analysis results. In: Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology Series, vol. 364, pp. 259–264. Springer, Heidelberg (2011)
Geman, O., Turcu, C.: Partitioning methods used in DBS treatments analysis results. In: Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, pp. 1788–1793 (2011)
Lungu, M.M., Bosancu, A., Geman, O.: Mini-review: human microbiome project – recent trends and future challenges. In: Proceedings of IEEE International Conference on E-Health and Bioengineering (EHB 2015), pp. 1–4 (2015)
Bucerzan, D., Raţiu, C.: Image processing with android steganography. In: Soft Computing Application, Proceedings of 6th International Workshop on Soft Computing Applications (SOFA 2014). Advances in Intelligent Systems and Computing, vol. 1, pp. 27–36. Springer International Publishing, Cham (2014). doi:10.1007/978-3-319-27179-8. eBook ISBN 978-3-319-18296-4, ISBN 978-3-319-27178-1
Bucerzan, D., Raţiu, C.: Contributions to steganographic techniques on mobile devices. In: Innovative Security Solutions for Information Technology and Communications. Lecture Notes in Computer Science, vol. 9522, pp. 242–252. Springer International Publishing, Cham (2016). doi:10.1007/978-3-319-27179. eBook ISBN 978-3-319-27179-8, ISBN 978-3-319-27178-1
Raţiu, C., Bucerzan, D., Manolescu, M.J.: SmartSteg: a new android based steganography application. Int. J. Comput. Commun. Control 8(5), 681–688 (2013). ISSN 1841-9836
Project: Analysis of novel risk factors influencing control of food intake and regulation of body weight. (PN-II-ID-PCE-2012-4-0608 no. 48/02.09.2013) (2016). www.eed.usv.ro/idei_48
Acknowledgement
This work was supported by the Romanian National Program (PN-II-ID-PCE-2012-4-0608 no. 48/02.09.2013), “Analysis of novel risk factors influencing control of food intake and regulation of body weight” [36].
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Geman, O., Chiuchisan, I., Covasa, M., Doloc, C., Milici, MR., Milici, LD. (2018). Deep Learning Tools for Human Microbiome Big Data. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_21
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