Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks
暂无分享,去创建一个
Philip S. Yu | Derek Reiman | Philip S Yu | Yang Dai | Ahmed A. Metwally | Ahmed A Metwally | Patricia W Finn | David L Perkins | Yang Dai | P. Finn | D. Perkins | Derek Reiman
[1] Francesco Asnicar,et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science , 2018 .
[2] Cesare Furlanello,et al. Phylogenetic convolutional neural networks in metagenomics , 2017, BMC Bioinformatics.
[3] Aly A. Khan,et al. Lactobacillus rhamnosus GG-supplemented formula expands butyrate-producing bacterial strains in food allergic infants , 2015, The ISME Journal.
[4] Derek Reiman,et al. Using convolutional neural networks to explore the microbiome , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[5] Gregory Ditzler,et al. Multi-Layer and Recursive Neural Networks for Metagenomic Classification , 2015, IEEE Transactions on NanoBioscience.
[6] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[7] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[8] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[9] L. T. Angenent,et al. Succession of microbial consortia in the developing infant gut microbiome , 2010, Proceedings of the National Academy of Sciences.
[10] C. Mackay,et al. Dietary Fiber and Bacterial SCFA Enhance Oral Tolerance and Protect against Food Allergy through Diverse Cellular Pathways. , 2016, Cell reports.
[11] Derek Reiman,et al. PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolution Neural Networks for Metagenomic Data , 2018, bioRxiv.
[12] Richard Hans Robert Hahnloser,et al. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.
[13] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[14] G. Weinstock,et al. A prospective microbiome‐wide association study of food sensitization and food allergy in early childhood , 2018, Allergy.
[15] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[17] Yun‐Cheol Na,et al. Faecalibacterium prausnitzii subspecies-level dysbiosis in the human gut microbiome underlying atopic dermatitis. , 2016, The Journal of allergy and clinical immunology.
[18] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[19] Li Yuan,et al. Altered Fecal Microbiota Composition Associated with Food Allergy in Infants , 2014, Applied and Environmental Microbiology.
[20] Tommi Vatanen,et al. Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans , 2016, Cell.
[21] G. Gerber,et al. A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis. , 2013, The Journal of allergy and clinical immunology.
[22] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[23] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[24] Mihai Pop,et al. Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection , 2016, Microbiome.
[25] Alexander Grishin,et al. Early-life gut microbiome composition and milk allergy resolution. , 2016, The Journal of allergy and clinical immunology.
[26] H. Akaike. Fitting autoregressive models for prediction , 1969 .
[27] D. Antonopoulos,et al. Commensal bacteria protect against food allergen sensitization , 2014, Proceedings of the National Academy of Sciences.
[28] Jianxin Shi,et al. Allergy associations with the adult fecal microbiota: Analysis of the American Gut Project , 2015, EBioMedicine.
[29] Botanic Gardens,et al. In Early Childhood , 2017 .
[30] Patricia W. Finn,et al. WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences , 2016, bioRxiv.
[31] P. Finn,et al. Microbiome: Allergic Diseases of Childhood , 2018 .
[32] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[33] Tommi Vatanen,et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. , 2015, Cell host & microbe.
[34] Alex Bateman,et al. An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.
[35] A. Knulst,et al. Fecal Microbiome and Food Allergy in Pediatric Atopic Dermatitis: A Cross-Sectional Pilot Study , 2018, International Archives of Allergy and Immunology.
[36] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[37] Luke R. Thompson,et al. Species-level functional profiling of metagenomes and metatranscriptomes , 2018, Nature Methods.
[38] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[39] R. Knight,et al. Moving pictures of the human microbiome , 2011, Genome Biology.
[40] Duy Tin Truong,et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling , 2015, Nature Methods.
[41] R. Kumar,et al. The Prevalence, Severity, and Distribution of Childhood Food Allergy in the United States , 2011, Pediatrics.
[42] Ahmed A. Metwally,et al. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. , 2016, Biochemical and biophysical research communications.
[43] David Vandyke,et al. Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems , 2015, EMNLP.
[44] Yang Dai,et al. MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies , 2018, Microbiome.
[45] B. Roe,et al. A core gut microbiome in obese and lean twins , 2008, Nature.