Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data

(2020) Jasmine Chong et al.   Nature Protocols

Biogeographic Patterns in Members of Globally Distributed and Dominant Taxa Found in Port Microbial Communities

(2020) Ryan B. Ghannam et al.   mSphere

Predicting postmortem interval based on microbial community sequences and machine learning algorithms

(2020) Ruina Liu et al.   ENVIRONMENTAL MICROBIOLOGY

Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes

(2020) Larissa Frühe et al.   MOLECULAR ECOLOGY

BiomMiner: An advanced exploratory microbiome analysis and visualization pipeline

(2020) Amirhossein Shamsaddini et al.   PLoS One

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

(2020) Begüm D. Topçuoğlu et al.   mBio

A t-SNE Based Classification Approach to Compositional Microbiome Data

(2020) Xueli Xu et al.   Frontiers in Genetics

Robustness, sensitivity and reproducibility of eDNA metabarcoding as an environmental biomonitoring tool in coastal salmon aquaculture – An inter-laboratory study

(2020) Verena Dully et al.   ECOLOGICAL INDICATORS

Applications of machine learning in drug discovery and development

(2019) Jessica Vamathevan et al.   NATURE REVIEWS DRUG DISCOVERY

Application of Machine Learning in Microbiology

(2019) Kaiyang Qu et al.   Frontiers in Microbiology

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

(2019) Evan Bolyen et al.   NATURE BIOTECHNOLOGY

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition

(2019) Jaron Thompson et al.   PLoS One

Microbial Similarity between Students in a Common Dormitory Environment Reveals the Forensic Potential of Individual Microbial Signatures

(2019) Miles Richardson et al.   mBio

A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

(2019) Yi-Hui Zhou et al.   Frontiers in Genetics

Machine Learning Interpretability: A Survey on Methods and Metrics

(2019) Diogo V. Carvalho et al.   Electronics

GMrepo: a database of curated and consistently annotated human gut metagenomes

(2019) Sicheng Wu et al.   NUCLEIC ACIDS RESEARCH

Microbiome composition and implications for ballast water classification using machine learning

(2019) William A. Gerhard et al.   SCIENCE OF THE TOTAL ENVIRONMENT

The art of using t-SNE for single-cell transcriptomics

(2019) Dmitry Kobak et al.   Nature Communications

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

(2019) Anna C. Belkina et al.   Nature Communications

An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

(2019) René Janßen et al.   MARINE POLLUTION BULLETIN

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

(2019) Jean-Marc Fellous et al.   Frontiers in Neuroscience

Deep learning models for bacteria taxonomic classification of metagenomic data

(2018) Antonino Fiannaca et al.   BMC BIOINFORMATICS

Taxonomy-aware feature engineering for microbiome classification

(2018) Mai Oudah et al.   BMC BIOINFORMATICS

Methods for interpreting and understanding deep neural networks

(2018) Grégoire Montavon et al.   DIGITAL SIGNAL PROCESSING

Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring

(2018) Tristan Cordier et al.   Molecular Ecology Resources

A global atlas of the dominant bacteria found in soil

(2018) Manuel Delgado-Baquerizo et al.   SCIENCE

Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

(2018) Aeriel Belk et al.   Genes

Decision support system for bioleaching processes

(2018) Cecilia Demergasso et al.   HYDROMETALLURGY

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

(2018) Marinka Zitnik et al.   Information Fusion

The gut microbiome: Relationships with disease and opportunities for therapy

(2018) Juliana Durack et al.   JOURNAL OF EXPERIMENTAL MEDICINE

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring

(2018) Tristan Cordier et al.   TRENDS IN MICROBIOLOGY

A communal catalogue reveals Earth’s multiscale microbial diversity

(2017) Luke R. Thompson et al.   NATURE

Accessible, curated metagenomic data through ExperimentHub

(2017) Edoardo Pasolli et al.   NATURE METHODS

Microbiome Tools for Forensic Science

(2017) Jessica L. Metcalf et al.   TRENDS IN BIOTECHNOLOGY

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

(2017) Hao-Xun Chang et al.   Frontiers in Microbiology

Microbiome Datasets Are Compositional: And This Is Not Optional

(2017) Gregory B. Gloor et al.   Frontiers in Microbiology

The human microbiome: an emerging tool in forensics

(2017) Jarrad T. Hampton-Marcell et al.   Microbial Biotechnology

A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval

(2017) Hunter R. Johnson et al.   PLoS One

Metagenomic applications in environmental monitoring and bioremediation

(2016) Stephen M. Techtmann et al.   JOURNAL OF INDUSTRIAL MICROBIOLOGY & BIOTECHNOLOGY

DADA2: High-resolution sample inference from Illumina amplicon data

(2016) Benjamin J Callahan et al.   NATURE METHODS

A random forest guided tour

(2016) Gérard Biau et al.   TEST

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

(2016) Edoardo Pasolli et al.   PLoS Computational Biology

A random forest guided tour

(2016) Gérard Biau et al.   TEST

Personalized Nutrition by Prediction of Glycemic Responses

(2015) David Zeevi et al.   CELL

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

(2015) Alex Goldstein et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Structure and function of the global ocean microbiome

(2015) S. Sunagawa et al.   SCIENCE

Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors

(2015) Mark B. Smith et al.   mBio

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses

(2014) Pier Luigi Buttigieg et al.   FEMS MICROBIOLOGY ECOLOGY

Random Whole Metagenomic Sequencing for Forensic Discrimination of Soils

(2014) Anastasia S. Khodakova et al.   PLoS One

Advances in monitoring environmental microbes

(2012) Terry C Hazen et al.   CURRENT OPINION IN BIOTECHNOLOGY

Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

(2012) J Gregory Caporaso et al.   ISME Journal

Search and clustering orders of magnitude faster than BLAST

(2010) Robert C. Edgar   BIOINFORMATICS

UniFrac: an effective distance metric for microbial community comparison

(2010) Catherine Lozupone et al.   ISME Journal

QIIME allows analysis of high-throughput community sequencing data

(2010) J Gregory Caporaso et al.   NATURE METHODS

Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

(2009) P. D. Schloss et al.   APPLIED AND ENVIRONMENTAL MICROBIOLOGY

Variability of the glycemic response to single food products in healthy subjects

(2009) Ruth Vrolix et al.   Contemporary Clinical Trials

A core gut microbiome in obese and lean twins

(2008) Peter J. Turnbaugh et al.   NATURE