<\/p>\n\n\n\n
The advancement of high\u2010throughput genomic assays has led to enormous growth in the availability of large\u2010scale biological datasets. Over the last two decades, these increasingly complex data have required statistical approaches that are more sophisticated than traditional linear models. Machine learning methodologies such as neural networks have yielded state\u2010of\u2010the\u2010art performance for prediction\u2010based tasks in many biomedical applications. However, a notable downside of these machine learning models is that they typically do not reveal how or why accurate predictions are made. In many areas of biomedicine, this \u201cblack box\u201d property can be less than desirable\u2014particularly when there is a need to perform in silico hypothesis testing about a biological system, in addition to justifying model findings for downstream decision\u2010making, such as determining the best next experiment or treatment strategy. Explainable and interpretable machine learning approaches have emerged to overcome this issue. While explainable methods attempt to derive post hoc understanding of what a model has learned, interpretable models are designed to inherently provide an intelligible definition of their parameters and architecture. Here, we review the model transparency spectrum moving from black box and explainable, to interpretable machine learning methodology. Motivated by applications in genomics, we provide background on the advances across this spectrum, detailing specific approaches in both supervised and unsupervised learning. Importantly, we focus on the promise of incorporating existing biological knowledge when constructing interpretable machine learning methods for biomedical applications. We then close with considerations and opportunities for new development in this space.<\/p>\n","protected":false},"excerpt":{"rendered":"
The advancement of high\u2010throughput genomic assays has led to enormous growth in the availability of large\u2010scale biological datasets. Over the last two decades, these increasingly complex data have required statistical approaches that are more sophisticated than traditional linear models. Machine learning methodologies such as neural networks have yielded state\u2010of\u2010the\u2010art performance for prediction\u2010based tasks in many […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Ashley Conard","user_id":"42849"},{"type":"text","value":"Alan DenAdel","user_id":0},{"type":"user_nicename","value":"Lorin Crawford","user_id":"39660"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"WIREs Computational Statistics","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Wiley Interdisciplinary Reviews: Computational 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