Cited by 109Computational Drug Discovery MethodsNeural Networks and ApplicationsAdvanced Memory and Neural Computing

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

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Pastur-Romay, Lucas, Cedrón, Francisco, Pazos, Alejandro, Porto-Pazos, Ana·2016·International Journal of Molecular Sciences

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.

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  1. [4]Puri M. Pathak Y. Sutariya V.K. Tipparaju S. Moreno W. Artificial Neural Network for Drug Design, Delivery and Disposition Elsevier Science Amsterdam, The Netherlands 2015
  2. [29]Lipton Z.C. A Critical Review of Recurrent Neural Networks for Sequence Learning Available online: http://arXiv Prepr arXiv1506.00019 (accessed on 13 May 2016)
  3. [40]Unterthiner T. Mayr A. Klambauer G. Steijaert M. Wegner J.K. Ceulemans H. Hochreiter S. Deep learning as an opportunity in virtual screening Proceedings of the Deep Learning Workshop at NIPS Montreal, QC, Canada 8–13 December 2014
  4. [43]Dahl G.E. Jaitly N. Salakhutdinov R. Multi-Task Neural Networks for QSAR Predictions Available online: http://arxiv.org/abs/1406.1231 (accessed on 13 May 2016) · arXiv
  5. [64]Pascanu R. Gulcehre C. Cho K. Bengio Y. How to construct deep recurrent neural networks Available online: http://arXiv Prepr arXiv1312.6026 (accessed on 13 May 2016)
  6. [75]Mead C. Conway L. Introduction to VLSI Systems Addison-Wesley Reading, MA, USA 1980 Volume 1080
  7. [79]Dehaene S. Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts Viking Press New York, NY, USA 2014
  8. [97]Diehl P.U. Pedroni B.U. Cassidy A. Merolla P. Neftci E. Zarrella G. TrueHappiness: Neuromorphic Emotion Recognition on Truenorth Available online: http://arxiv.org/abs/1601.04183 (accessed on 13 May 2016) · arXiv
  9. [123]Koob A. The Root of Thought: Unlocking Glia—The Brain Cell That Will Help Us Sharpen Our Wits, Heal Injury, and Treat Brain Disease FT Press Upper Saddle River, NJ, USA 2009
  10. [145]Porto A. Pazos A. Araque A. Artificial neural networks based on brain circuits behaviour and genetic algorithms Computational Intelligence and Bioinspired Systems Springer Berlin, Germany 2005 99 106

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Authors
Pastur-Romay, L., Cedrón, F., Pazos, A., & Porto-Pazos, A.
Affiliations
Universidade da Coruña (ES)
Corresponding author
Porto-Pazos, Ana
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2016-08-11
Volume
17
Issue
8
Pages
1313
ISSN
1422-0067 (ISSN-L)
OpenAlex
W2517582793
Cited by
109
References
127
Citations by year
2026: 2 · 2025: 11 · 2024: 10 · 2023: 15 · 2022: 12 · 2021: 11 · 2020: 9 · 2019: 11 · 2018: 17 · 2017: 6 · 2016: 5
FWCI
1.00
Type
journal article
Language
en
Open access
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • RUC (Universidade Da Coruña) (submittedVersion, cc-by)
Funders
  • European Commission (2013-2016)
  • Xunta de Galicia (R2014/039, GRC2014/049)
  • Instituto de Salud Carlos III (2013-2016, PI13/00280)
  • Spanish National Plan for Scientific and Technical Research and Innovation
  • Center for Innovative Medicine
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Keywords
deep neural network, neuromorphic hardware, drug design, virtual screening, QSAR, protein structure prediction, SpiNNaker, astrocytes, glial cells, bioinformatics
MeSH terms
Algorithms, Animals, Humans, Neural Networks, Computer, Computational Biology, Quantitative Structure-Activity Relationship

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