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Int. J. Mol. Sci. 2016, 17(8), 1313; doi:10.3390/ijms17081313

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

1
Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain
2
Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Humberto González-Díaz, Roberto Todeschini, Alejandro Pazos Sierra and Sonia Arrasate Gil
Received: 16 May 2016 / Revised: 14 July 2016 / Accepted: 25 July 2016 / Published: 11 August 2016
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Abstract

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. View Full-Text
Keywords: artificial neural networks; artificial neuron–astrocyte networks; tripartite synapses; deep learning; neuromorphic chips; big data; drug design; Quantitative Structure–Activity Relationship; genomic medicine; protein structure prediction artificial neural networks; artificial neuron–astrocyte networks; tripartite synapses; deep learning; neuromorphic chips; big data; drug design; Quantitative Structure–Activity Relationship; genomic medicine; protein structure prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pastur-Romay, L.A.; Cedrón, F.; Pazos, A.; Porto-Pazos, A.B. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications. Int. J. Mol. Sci. 2016, 17, 1313.

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