Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation

Role of gut microbiota in type 2 diabetes pathophysiology

(2020) Manoj Gurung et al.   EBioMedicine

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

(2020) Begüm D. Topçuoğlu et al.   mBio

Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases

(2019) Serena Sanna et al.   NATURE GENETICS

Longitudinal multi-omics of host–microbe dynamics in prediabetes

(2019) Wenyu Zhou et al.   NATURE

A field guide for the compositional analysis of any-omics data

(2019) Thomas P Quinn et al.   GigaScience

Aberrant intestinal microbiota in individuals with prediabetes

(2018) Kristine H. Allin et al.   DIABETOLOGIA

Best practices for analysing microbiomes

(2018) Rob Knight et al.   NATURE REVIEWS MICROBIOLOGY

On the overestimation of random forest’s out-of-bag error

(2018) Silke Janitza et al.   PLoS One

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

(2017) Markku Laakso et al.   JOURNAL OF LIPID RESEARCH

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

(2017) Marvin N. Wright et al.   Journal of Statistical Software

Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data

(2016) Gregory B. Gloor et al.   CANADIAN JOURNAL OF MICROBIOLOGY

DADA2: High-resolution sample inference from Illumina amplicon data

(2016) Benjamin J Callahan et al.   NATURE METHODS

Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity

(2016) Alexandra Zhernakova et al.   SCIENCE

Population-level analysis of gut microbiome variation

(2016) Gwen Falony et al.   SCIENCE

Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights

(2016) Edoardo Pasolli et al.   PLoS Computational Biology

zCompositions — R package for multivariate imputation of left-censored data under a compositional approach

(2015) Javier Palarea-Albaladejo et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

The role of acetic acid on glucose uptake and blood flow rates in the skeletal muscle in humans with impaired glucose tolerance

(2015) P Mitrou et al.   EUROPEAN JOURNAL OF CLINICAL NUTRITION

Acetic Acid Bacteria: Physiology and Carbon Sources Oxidation

(2013) Dhouha Mamlouk et al.   INDIAN JOURNAL OF MICROBIOLOGY

Is type 2 diabetes a category error?

(2013) Edwin AM Gale   LANCET

Gut metagenome in European women with normal, impaired and diabetic glucose control

(2013) Fredrik H. Karlsson et al.   NATURE

Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance

(2013) Xiuying Zhang et al.   PLoS One

Prediabetes: a high-risk state for diabetes development

(2012) Adam G Tabák et al.   LANCET

A metagenome-wide association study of gut microbiota in type 2 diabetes

(2012) Junjie Qin et al.   NATURE

Structure, function and diversity of the healthy human microbiome

(2012)   NATURE

Molecular Characterisation of the Faecal Microbiota in Patients with Type II Diabetes

(2010) Xiaokang Wu et al.   CURRENT MICROBIOLOGY

Changes in Insulin Sensitivity and Insulin Release in Relation to Glycemia and Glucose Tolerance in 6,414 Finnish Men

(2009) A. Stancakova et al.   DIABETES

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

(2009) M. N. Price et al.   MOLECULAR BIOLOGY AND EVOLUTION

Bacterial Community Variation in Human Body Habitats Across Space and Time

(2009) E. K. Costello et al.   SCIENCE