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.
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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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