Dr. Daniel Urda published and presented a second paper at the 14th International Work-Conference on Artificial Neural Networks held in Cádiz from the 14th-16th of June.
Deep learning (also known as deep structured learning or hierarchical learning) is the application to learning tasks of artificial neural networks (ANNs) that contain more than one hidden layer, and deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been successfully applied by companies such as Google, Facebook or Microsoft to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts.
This conference on Artificial Neural Networks provided a discussion forum for scientists, engineers, educators and students about the latest discoveries and realizations in the foundations, theory, models and applications of systems inspired by nature, using computational intelligence methodologies, as well as in emerging areas related to the above items.
Dr. Urda´s paper presented a first approach of how deep learning can be used to analyze RNA-Seq gene expression data in order to predict the vital status of a patient at some point in the future.
The discussion which ensued during the Q&A period was very lively and produced useful feedback and ideas to ensure further progress in this compelling line of research.