Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring
| Autor: | Stephen M. Techtmann, Ryan B. Ghannam |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
SVM
Support Vector Machines Computer science SML Supervised Machine Learning Biophysics Review Article Marker genes Machine learning computer.software_genre Biochemistry 03 medical and health sciences 0302 clinical medicine Microbial ecology USML Unsupervised Machine Learning Structural Biology Environmental monitoring Genetics Leverage (statistics) Microbiome 16S rRNA AUC Area Under the Curve GB Gradient Boosting Environmental quality ComputingMethodologies_COMPUTERGRAPHICS Forensics 030304 developmental biology Interpretability 0303 health sciences tSNE t-distributed Stochastic Neighbor Embedding business.industry ROC Receiver Operating Characteristic PCoA Principal Coordinate Analysis ASV Amplicon Sequence Variant ANN Artificial Neural Networks Computer Science Applications Microbial population biology ML Machine Learning Metagenomics 030220 oncology & carcinogenesis Artificial intelligence business RF Random Forests computer TP248.13-248.65 Biotechnology |
| Zdroj: | Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 1092-1107 (2021) Computational and Structural Biotechnology Journal |
| ISSN: | 2001-0370 |
| DOI: | 10.1016/j.csbj.2021.01.028 |
| Popis: | Graphical abstract Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities. |
| Databáze: | OpenAIRE |
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