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  • Review Article
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Machine learning for microbiologists

Nature Reviews Microbiology volume 22, pages 191–205 (2024)Cite this article

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Abstract

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

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Fig. 1: General workflow and examples for machine learning applications in microbiology.
Fig. 2: Practical examples of unsupervised learning tasks.
Fig. 3: Training and testing strategies for supervised machine learning model evaluation.
Fig. 4: Supervised machine learning evaluation methods in a real-data example.

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Acknowledgements

The authors thank all members of the Computational Metagenomics laboratory, the Waldron laboratory and the Structured Machine Learning Group for their feedback and suggestions. This work was supported by the European Research Council (ERC-STG project MetaPG-716575 and ERC-CoG microTOUCH-101045015) to N.S., by the European Union’s Horizon 2020 programme (IHMCSA-964590) to N.S. and F.A., and by the European Union under NextGenerationEU (Interconnected Nord-Est Innovation programme (INEST)) to N.S. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or The European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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  1. These authors contributed equally: Francesco Asnicar, Andrew Maltez Thomas.

Authors and Affiliations

  1. Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy

    Francesco Asnicar, Andrew Maltez Thomas, Levi Waldron & Nicola Segata

  2. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

    Andrea Passerini

  3. Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA

    Levi Waldron

  4. Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy

    Nicola Segata

Authors
  1. Francesco Asnicar
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  2. Andrew Maltez Thomas
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  3. Andrea Passerini
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  4. Levi Waldron
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Contributions

N.S., F.A. and A.M.T. contributed equally to all aspects of the article. A.P. contributed substantially to discussion of the content and reviewed and/or edited the manuscript before submission. L.W. contributed substantially to discussion of the content, writing, and review and/or editing of the manuscript before submission.

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Correspondence to Levi Waldron or Nicola Segata.

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Nature Reviews Microbiology thanks Elhanan Borenstein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Accuracy

The number of correct classification predictions (true positives + true negatives) divided by the total number of predictions (true positives + true negatives + false positives + false negatives).

Area under the ROC curve

(AUC-ROC). A number between 0 and 1 that is obtained by integrating the receiver operating characteristic (ROC) curve over the different classification thresholds and that represents the ability of a binary classification model to discriminate between two classes, where 0.5 and 1 represent the random and perfect classification of the samples, respectively.

Cross-validation

An approach to provide robust performance estimates of how well the trained model generalizes on new data by splitting a data set into multiple subsets and iteratively training on some subsets and testing on the others.

Data set

A set of examples with input features and target values (if available), used to train and/or evaluate machine learning models, that can be divided into three non-overlapping subsets: training, validation and test sets. It is crucial to ensure that the same example is not present in both training and test (or validation) sets for a correct estimate of the generalizability of the learned model.

Decision tree

A non-parametric supervised learning method with a hierarchical tree structure to represent a set of if–then–else rules for different conditions. The internal nodes define conditions, and the leaves represent outputs.

Example

A processed version of the microbiological sample, including features and, possibly, targets.

Features

The microbiological data information extracted from the samples that are provided as input to the machine learning model.

Least absolute shrinkage and selection operator

(LASSO). A linear model approach that performs both variable selection and regularization (stabilization of regression coefficients) and tends to give solutions with few non-zero coefficients, to reduce the number of features and enhance the interpretability of the model.

Leave-one-data set-out

(LODO). An approach used to estimate model generalizability across data sets, that can be employed if multiple different data sets are available.

Model

A mathematical object with appropriately set parameters used to make predictions.

Naive Bayes

A supervised learning algorithm based on the application of Bayes’ theorem with the ‘naive’ assumption that all features are independent.

Neural network

A model with at least one hidden layer, a set of unobserved variables called ‘neurons’ derived from input features. Deep neural networks contain at least two hidden layers, where each neuron in a hidden layer connects to all the neurons of the next hidden layer. Combining many hidden layers and their interconnections enable modelling complex and non-linear relationships between input features and target values.

Precision

A metric for classification models that measures the fraction of true positive examples over the set of examples predicted as positives (true positives / (true positives + false positives)).

Random forest

An ensemble method that relies on a collection of independently trained decision tree models whose predictions are then aggregated to make one single prediction.

Recall

A metric for classification models that measures the fraction of true positive examples over the set of positive examples, also known as coverage (true positives / (true positives + false negatives)).

Receiver operating characteristic (ROC) curve

Generally plotted as a graph between the true positive rate and the false positive rate at different classification thresholds for evaluating a binary classification model, the curve’s shape reflects the ability of the binary classification model to separate the two classes.

Samples

Original items, for example microbiological entities, from which features data and target values are derived.

Supervised machine learning

An algorithm that trains a model to predict the target based on input features, resulting in a trained model capable of classifying new and unseen samples using the same set of features.

Support vector machines

(SVMs). A set of supervised learning prediction methods based on statistical learning theory that aims to maximize the boundary between the positive and negative classes.

Target value

A priori defined classes or quantities of microbiological interest (for example, case or control labels, Gram positive or negative staining, optimal pH values for bacterial growth) associated with examples, that are available only at training time and need to be predicted at test time from the features alone.

Test set

The (sub)set of a data set used for the final evaluation of the trained model or for which the outcomes of interest are not known and should be predicted by the trained model.

Training set

The (sub)set of a data set that is used for training a machine learning model.

Unsupervised machine learning

An algorithm that trains a model based solely on input features to derive patterns without further knowledge about the samples from which features were extracted.

Validation set

The (sub)set of a data set used to evaluate a trained model.

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Asnicar, F., Thomas, A.M., Passerini, A. et al. Machine learning for microbiologists. Nat Rev Microbiol 22, 191–205 (2024). https://doi.org/10.1038/s41579-023-00984-1

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