Performance Analysis of Deep Neural Networks in piRNAs Classification
Modern machine learning techniques, such as Deep Learning, have been successful in many complex Bioinformatics tasks. The capacity of Deep Neural Networks to handle large volumes of data has made them essential tools for multiple areas of knowledge. However, developing the best model for a given task is a hard work. Deep Neural Networks have a very large number of hyperparameters, making them as powerful as complex to be adjusted. Therefore, in order to better understand the behavior of Deep Neural Networks when applied to biological data, we present in this paper a performance analysis of a Deep Feedforward Network in piRNAs classification. Different configurations of activation functions, initialization of weights, number of layers and learning rate are experienced. The effects of different hyperparameters are discussed and certain organizations are proposed for similar domains of data.
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