Research Area:  Machine Learning
Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.
Keywords:  
Bioinformatics
Medicine
Deep Learning
Machine Learning
Author(s) Name:  Davide Bacciu, Paulo J.G. Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido
Journal name:  Computer Science
Conferrence name:  
Publisher name:  arXiv:1802.09791
DOI:  10.48550/arXiv.1802.09791
Volume Information:  
Paper Link:   https://arxiv.org/abs/1802.09791