Deep Learning Tools for Human Microbiome Big Data

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.

[1]  Oana Geman,et al.  Data mining and knowledge discovery tools for human microbiome big data , 2016, 2016 6th International Conference on Computers Communications and Control (ICCCC).

[2]  Emil Simion,et al.  Innovative Security Solutions for Information Technology and Communications , 2015, Lecture Notes in Computer Science.

[3]  Emily B. Hollister,et al.  From Prediction to Function Using Evolutionary Genomics: Human-Specific Ecotypes of Lactobacillus reuteri Have Diverse Probiotic Functions , 2014, Genome biology and evolution.

[4]  Wei Song,et al.  Targeting gut microbiota as a possible therapy for diabetes. , 2015, Nutrition research.

[5]  Rob Knight,et al.  Analysis of composition of microbiomes: a novel method for studying microbial composition , 2015, Microbial ecology in health and disease.

[6]  T. de Wouters,et al.  Up-regulation of intestinal type 1 taste receptor 3 and sodium glucose luminal transporter-1 expression and increased sucrose intake in mice lacking gut microbiota , 2011, British Journal of Nutrition.

[7]  Oana Geman,et al.  Mini-review: Human Microbiome project — Recent trends and future challenges , 2015, 2015 E-Health and Bioengineering Conference (EHB).

[8]  Oana Geman.  Nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier for automatic assessing of tremor severity , 2013, 2013 E-Health and Bioengineering Conference (EHB).

[9]  Susan M. Huse,et al.  Oligotyping analysis of the human oral microbiome , 2014, Proceedings of the National Academy of Sciences.

[10]  C. Huttenhower,et al.  Inflammatory bowel disease as a model for translating the microbiome. , 2014, Immunity.

[11]  H. Costin,et al.  Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier , 2014 .

[12]  Oana Geman,et al.  Partitioning methods used in DBS treatments analysis results , 2011, The 2011 International Joint Conference on Neural Networks.

[13]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[14]  Masashi Yanagisawa,et al.  Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41 , 2008, Proceedings of the National Academy of Sciences.

[15]  R. Ismagilov,et al.  Individually addressable arrays of replica microbial cultures enabled by splitting SlipChips. , 2014, Integrative biology : quantitative biosciences from nano to macro.

[16]  R. Ismagilov,et al.  Gene-targeted microfluidic cultivation validated by isolation of a gut bacterium listed in Human Microbiome Project's Most Wanted taxa , 2014, Proceedings of the National Academy of Sciences.

[17]  F. Duca,et al.  Current and emerging concepts on the role of peripheral signals in the control of food intake and development of obesity , 2012, British Journal of Nutrition.

[18]  Curtis Huttenhower,et al.  Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission , 2014, The ISME Journal.

[19]  Oana Geman.  Data Mining Tools Used in Deep Brain Stimulation - Analysis Results , 2011, EANN/AIAI.

[20]  Oldrich Vysata,et al.  Towards an inclusive Parkinson's screening system , 2014, 2014 18th International Conference on System Theory, Control and Computing (ICSTCC).

[21]  V. Tremaroli,et al.  Functional interactions between the gut microbiota and host metabolism , 2012, Nature.

[22]  Qiang Feng,et al.  The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment , 2015, Nature Medicine.

[23]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[24]  Nilanjan Dey,et al.  Haralick Features Based Automated Glaucoma Classification Using Back Propagation Neural Network , 2014, FICTA.

[25]  Denis Roy,et al.  Probiotics as Complementary Treatment for Metabolic Disorders , 2015, Diabetes & metabolism journal.

[26]  Yoshimasa Tanaka,et al.  Risk Factors Contributing to Type 2 Diabetes and Recent Advances in the Treatment and Prevention , 2014, International journal of medical sciences.

[27]  Adrian Streinu-Cercel,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 , 2013, BMC Infectious Diseases.

[28]  Patrice D Cani,et al.  Diabetes, obesity and gut microbiota. , 2013, Best practice & research. Clinical gastroenterology.

[29]  Dominic Bucerzan,et al.  SmartSteg: A New Android Based Steganography Application , 2013, Int. J. Comput. Commun. Control.

[30]  Francesco Salvatore,et al.  The role of the gut microbiome in the healthy adult status. , 2015, Clinica chimica acta; international journal of clinical chemistry.