Machine learning methods for microbiome studies
暂无分享,去创建一个
[1] Jens Roat Kultima,et al. Potential of fecal microbiota for early‐stage detection of colorectal cancer , 2014 .
[2] William A. Walters,et al. QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.
[3] X. Xu,et al. Association Between Gut Microbiota and Autism Spectrum Disorder: A Systematic Review and Meta-Analysis , 2019, Front. Psychiatry.
[4] Nathan H. Lents,et al. A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval , 2016, PloS one.
[5] Jesse R. Zaneveld,et al. Normalization and microbial differential abundance strategies depend upon data characteristics , 2017, Microbiome.
[6] Martin Hartmann,et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities , 2009, Applied and Environmental Microbiology.
[7] T. Spector,et al. Interplay between the human gut microbiome and host metabolism , 2019, Nature Communications.
[8] Gang Niu,et al. Analysis of Learning from Positive and Unlabeled Data , 2014, NIPS.
[9] Horst Zitzelsberger,et al. Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. , 2012, Journal of proteome research.
[10] F. Ryan. Application of machine learning techniques for creating urban microbial fingerprints , 2019, Biology Direct.
[11] Xiaoli Li,et al. Ensemble Positive Unlabeled Learning for Disease Gene Identification , 2014, PloS one.
[12] J. T. Curtis,et al. An Ordination of the Upland Forest Communities of Southern Wisconsin , 1957 .
[13] J. Ashkani,et al. Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes , 2016, Scientific Reports.
[14] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[15] Alice C. McHardy,et al. MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples , 2018, Bioinformatics.
[16] Y. Ohashi,et al. Taxonomic classification for microbiome analysis, which correlates well with the metabolite milieu of the gut , 2018, BMC Microbiology.
[17] Chee Keong Kwoh,et al. Positive-unlabeled learning for disease gene identification , 2012, Bioinform..
[18] H. Becher,et al. Clustering of Subgingival Microbiota Reveals Microbial Disease Ecotypes Associated with Clinical Stages of Periodontitis in a Cross-Sectional Study , 2017, Front. Microbiol..
[19] Rafael A. Irizarry,et al. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses , 2017, Nature Communications.
[20] J. Caporaso,et al. Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota , 2019, Scientific Reports.
[21] W. Garrett,et al. Gut microbiota, metabolites and host immunity , 2016, Nature Reviews Immunology.
[22] Zhuye Jie,et al. Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance , 2013, PloS one.
[23] G. Hold,et al. Autism Spectrum Disorder and the Gut Microbiota in Children: A Systematic Review , 2020, Annals of Nutrition and Metabolism.
[24] Liping Zhao,et al. The gut microbiota, obesity and insulin resistance. , 2013, Molecular aspects of medicine.
[25] S. Horvath,et al. Global histone modification patterns predict risk of prostate cancer recurrence , 2005, Nature.
[26] Y. Nam,et al. Progress of analytical tools and techniques for human gut microbiome research , 2018, Journal of Microbiology.
[27] Sang-Uk Seo,et al. Role of the gut microbiota in immunity and inflammatory disease , 2013, Nature Reviews Immunology.
[28] Evgeny Putin,et al. Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects , 2018, bioRxiv.
[29] J. Raes,et al. The neuroactive potential of the human gut microbiota in quality of life and depression , 2019, Nature Microbiology.
[30] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[31] A. Baghestani,et al. How to control confounding effects by statistical analysis , 2012, Gastroenterology and hepatology from bed to bench.
[32] Patrice D Cani,et al. Diabetes, obesity and gut microbiota. , 2013, Best practice & research. Clinical gastroenterology.
[33] Young-Mo Kim,et al. Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice , 2019, Cell.
[34] Charles Elkan,et al. Learning classifiers from only positive and unlabeled data , 2008, KDD.
[35] D B Rubin,et al. Multiple imputation in health-care databases: an overview and some applications. , 1991, Statistics in medicine.
[36] K. Alviña,et al. The role of inflammation and the gut microbiome in depression and anxiety , 2019, Journal of neuroscience research.
[37] J. Segre,et al. The human microbiome: our second genome. , 2012, Annual review of genomics and human genetics.
[38] R. Morrison,et al. Study design, precision, and validity in observational studies. , 2009, Journal of palliative medicine.
[39] Michael G Kenward,et al. Multiple imputation: current perspectives , 2007, Statistical methods in medical research.
[40] P. Bork,et al. Richness of human gut microbiome correlates with metabolic markers , 2013, Nature.
[41] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[42] Anne-Laure Boulesteix,et al. AUC-RF: A New Strategy for Genomic Profiling with Random Forest , 2011, Human Heredity.
[43] R. Knight,et al. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. , 2009, Genome research.
[44] Radu Marculescu,et al. MetaNN: Accurate Classification of Host Phenotypes From Metagenomic Data Using Neural Networks , 2018, BCB.
[45] Soumen Roy,et al. Microbiota: a key orchestrator of cancer therapy , 2017, Nature Reviews Cancer.
[46] Kumardeep Chaudhary,et al. Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer , 2017, Scientific Reports.
[47] J. Dushoff,et al. Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization , 2012, PloS one.
[48] Qiang Feng,et al. A metagenome-wide association study of gut microbiota in type 2 diabetes , 2012, Nature.
[49] Taesung Park,et al. Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value , 2016, Journal of gastroenterology and hepatology.
[50] Francesco Asnicar,et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 , 2019, Nature Biotechnology.
[51] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[52] R. Lorenz,et al. Gut Microbiota and Obesity , 2012, Current Obesity Reports.
[53] Gang Niu,et al. Positive-Unlabeled Learning with Non-Negative Risk Estimator , 2017, NIPS.
[54] E. Mardis,et al. An obesity-associated gut microbiome with increased capacity for energy harvest , 2006, Nature.
[55] R. Knight,et al. Quantitative and Qualitative β Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities , 2007, Applied and Environmental Microbiology.
[56] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[57] Michael W Taylor,et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges , 2011, The ISME Journal.
[58] Michael Zimmermann,et al. Separating host and microbiome contributions to drug pharmacokinetics and toxicity , 2019, Science.
[59] Brian Munsky,et al. Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition , 2019 .