Research Area: Machine Learning
Deep learning is a subfield of machine learning that considers computational models with multiple processing layers [1, 3, 6]. At the core of all deep learning approaches lies ‘representation learning’: the models automatically learn a representation of the input data without the explicit guidance of a domain expert. Low-level features (such as edges in image data) are moved forward to the next layer where higher level, more abstract features (such as shapes) are extracted. This feature extraction is based on nonlinear functions in the processing units. Thereby, deep learning is capable of discovering the intrinsic hierarchies in the training data that can be exploited for a variety of analytical tasks. Most deep learning methods are designed for supervised classification, that is tasks for which an input–output mapping has to be learned from labeled training data. The success and failure of predictive models crucially depend on how well the hierarchical structures can be captured. ‘Deep neural networks’, such as multilayer feed-forward perceptrons, convolutional neural networks and recurrent neural networks, are particularly good at this task.
Keywords:
Deep Learning
Bioinformatics
Biomedicine
Author(s) Name: Daniel Berrar, Werner Dubitzky
Journal name: Briefings in Bioinformatics
Conferrence name:
Publisher name: Oxford University Press
DOI: 10.1093/bib/bbab087
Volume Information: Volume 22, Issue 2, March 2021, Pages 1513–1514
