Prediction of Bioprocess Production Using Deep Neural Network Method
Abstract: Deep learning
enhanced the state-of-the-art methods in genomics allows it to be used in analysing
the biological data with high prediction. The training process of neural
network with severalhidden layers which has been facilitated by deep learning
has been subjected into increased interest inachieving remarkable results in
various fields. Thus, the extraction of bioprocess production can beimplemented
by pathway prediction in genomic metabolic network in eschericia coli. As
metabolicengineering involves the manipulation of genes which have the
potential to increase the yield of metabolite production. A mathematical model
of this network is the foundation for the development of computational procedure
that directs genetic manipulations that would eventually lead to optimized
bioprocess production. Due to the ability of deep learning to be well suited in
terms of genomics, modelling forbiological network can be implemented. Each
layer reveal the insight of biological network which enable pathway analysis to
be implemented in order to extract the target bioprocess production. In this study,
deep neural network has been to identify any set of gene deletion models that
offers optimal results in xylitol production and its growth yield.
Keywords: deep learning, deep
neural network, bioprocess production, metabolic engineering, gene deletion
Author: Amirah Baharin
Journal Code: jptkomputergg170082
