Abstract
AbstractThe genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.
Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Genetics
Reference87 articles.
1. Adams SM, Feroze H, Nguyen T et al (2020) Genome wide epistasis study of on-statin cardiovascular events with iterative feature reduction and selection. J Pers Med. https://doi.org/10.3390/jpm10040212
2. Alaa AM, Bolton T, Angelantonio ED et al (2019) Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS One 14:e0213653. https://doi.org/10.1371/journal.pone.0213653
3. Alaa AM, van der Schaar M (2018a) AutoPrognosis: automated clinical prognostic modeling via Bayesian optimization with structured kernel learning. In: International conference on machine learning. PMLR, pp 139–148. http://proceedings.mlr.press/v80/alaa18b.html
4. Alaa AM, van der Schaar M (2018b) Prognostication and risk factors for cystic fibrosis via automated machine learning. Sci Rep 8:11242. https://doi.org/10.1038/s41598-018-29523-2
5. Alakwaa FM, Chaudhary K, Garmire LX (2018) Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J Proteome Res 17:337–347. https://doi.org/10.1021/acs.jproteome.7b00595
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献