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GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction

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Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1260))

Abstract

We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs or window-based data using data structures to efficiently represent input–output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF.

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Acknowledgements

We gratefully acknowledge the financial support provided by the National Institutes of Health (NIH) through Grants R01GM072014 and R01GM073095 and the National Science Foundation through Grant NSF MCB 1071785. Both authors would like to thank the organizers of CASP10 conference in Gaeta, Italy, for inviting them to the conference and providing free registration to EF. EF would also like to thank Yaoqi Zhou and Keith Dunker for hosting him at IUPUI and general discussions.

Author information

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA

    Eshel Faraggi

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, Ohio, 43215, USA

    Eshel Faraggi

  3. Physics Division, Research and Information Systems, LLC, Carmel, Indiana, 46032, USA

    Eshel Faraggi

  4. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, Ohio, 43215, USA

    Andrzej Kloczkowski

  5. Department of Pediatrics, The Ohio State University, Columbus, Ohio, 43215, USA

    Andrzej Kloczkowski

Authors
  1. Eshel Faraggi
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  2. Andrzej Kloczkowski
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Corresponding authors

Correspondence to Eshel Faraggi or Andrzej Kloczkowski .

Editor information

Editors and Affiliations

  1. Chemistry Department, Oxford University, UK, and University of Victoria, Victoria, British Columbia, Canada

    Hugh Cartwright

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Faraggi, E., Kloczkowski, A. (2015). GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_10

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  • DOI: https://doi.org/10.1007/978-1-4939-2239-0_10

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2238-3

  • Online ISBN: 978-1-4939-2239-0

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