The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential.
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The authors thank P. Baldi, M. Beer, A. Ben-Hur, J. Ernst, E. Eskin, G. Haliburton, H. Huang, S.-I. Lee, M. Libbrecht, J. Majewski, Q. Morris, S. Mostafavi, J.-P. Vert, W. Wang, B. Yu and M. Zitnik for recommending examples and for helpful suggestions on how to review this topic.
The authors declare no competing interests.
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Interactive notebooks of the pitfalls discussed in this Review: https://github.com/shwhalen/ml-pitfalls
Also known as ‘samples’ or ‘observations’. The primary data objects being manipulated by a machine learning system. They are the basic units being measured.
- Training set
Examples and associated outcomes that are used to fit a supervised machine learning model.
- Test set
Examples and associated outcomes that are used to evaluate model performance. Training and test sets are disjoint.
The value of one example does not depend on the value of others.
- Identically distributed
Generated by the same underlying distribution, with a particular mean, variance and shape.
- Generalization error
A measure of how accurately a model predicts outcomes in data it has never seen before.
- Prediction set
A third set of examples whose associated outcomes are truly not known, where a fitted model is applied to make predictions. Also known as a prospective validation set.
- True negatives
Negatives whose labels are correctly predicted.
Properties of a given example, for example, the gene expression values associated with a gene or the sequence patterns associated with a genomic window. Also known as ‘covariates’.
Outcomes are what we want to predict in supervised learning, for example, the functional class assigned to a gene or the binary classification of whether a given genomic window contains a promoter. Categorical outcomes are often referred to as ‘labels’. In regression settings, the outcome is a real number.
- Ascertainment bias
Examples in a study are not representative of the general population.
- Adversarial learning
Machine learning techniques for improving model robustness to distributional differences, such as those caused by batch effects or other confounders.
Positives are examples with the outcome of interest in a binary classifier.
Negatives are examples with the alternative outcome in a binary classifier. In genomics, negatives often outnumber positives.
A variable causally influenced by two variables, for example, both a feature and the outcome in predictive modelling.
Unsupervised learning, where there is no measured outcome, although the cluster assignment is an estimate of an unobserved label. The goal is to organize examples on the basis of pairwise similarities of their features, for example, into groups (‘clusters’) or a hierarchical tree.
- False negatives
Positives whose labels are incorrectly predicted as negative.
- True positives
Positives whose labels are correctly predicted.
- False positives
Negatives whose labels are incorrectly predicted as positive.
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Whalen, S., Schreiber, J., Noble, W.S. et al. Navigating the pitfalls of applying machine learning in genomics. Nat Rev Genet 23, 169–181 (2022). https://doi.org/10.1038/s41576-021-00434-9
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