Year: 2014
Type: editorial
Abstract: Machine learning is a critical component of any biological data mining. Given its established advantages, there remain challenges that need to be addressed before it can be considered practical and persuasive. Thus, not quite The Tempest (Act V, Scene One), above, but Shakespeare would still have understood: learning machines have achieved stunning success in a stunning range of areas, but they are still often—correctly—seen as strange and mysterious. They make predictions {tumor, not tumor} with ease and rapidity, but how do we understand the forecasts? How were the forecasts made, and how do they apply to this patient, with this set of symptoms and exposure factors? How does a low mean square error translate into guidance for patient treatment and care? How is any machine translated?
Source: BioData Mining
Authors: James D Malley, Karen G Malley, Jason H Moore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W2110976973