Abstract
We have studied the ability of three types of neural networks to predict the closeness of a given protein model to the native structure associated with its sequence. We show that a partial combination of the Levenberg–Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural network improves its performance. Additionally, we find that the hybrid method we propose was the most robust in the sense that other configurations of it experienced less decline in comparison to the other methods. We find that the hybrid networks also undergo more fluctuations on the path to convergence. We propose that these fluctuations allow for better sampling. Overall we find it may be beneficial to treat different parts of a neural network with varied computational approaches during optimization.
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Acknowledgements
This work was funded in part by a National Science Foundation grant DBI 1661391, and National Institutes of Health grants R01 GM127701 and R01 GM127701-01S1. Additional support provided by the Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute, and through the National Science Foundation under Grant No. CNS-0521433.
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Faraggi, E., Jernigan, R.L., Kloczkowski, A. (2021). A Hybrid Levenberg–Marquardt Algorithm on a Recursive Neural Network for Scoring Protein Models. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_15
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DOI: https://doi.org/10.1007/978-1-0716-0826-5_15
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