Abstract
Biology has become a data driven science largely due to the technological advances that have generated large volumes of data. To extract meaningful information from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.
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Acknowledgments
I would like to thank IUSB for funding this work. This work is also supported partly by NSF award 1726218.
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Nair, T.M. (2021). Building and Interpreting Artificial Neural Network Models for Biological Systems. 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_8
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DOI: https://doi.org/10.1007/978-1-0716-0826-5_8
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