DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals
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Date
2023-01
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Abstract
The impact of climate change has been alarming for the crop growth. The
extreme weather conditions can stress the crops and reduce the yield of major
crops belonging to Poaceae family too, that sustains 50% of the world’s food
calorie and 20% of protein intake. Computational approaches, such as artificial
intelligence-based techniques have become the forefront of prediction-based
data interpretation and plant stress responses. In this study, we proposed a
novel activation function, namely, Gaussian Error Linear Unit with Sigmoid
(SIELU) which was implemented in the development of a Deep Learning (DL)
model along with other hyper parameters for classification of unknown abiotic
stress protein sequences from crops of Poaceae family. To develop this
models, data pertaining to four different abiotic stress (namely, cold, drought,
heat and salinity) responsive proteins of the crops belonging to poaceae family
were retrieved from public domain. It was observed that efficiency of the DL
models with our proposed novel SIELU activation function outperformed the
models as compared to GeLU activation function, SVM and RF with 95.11%,
80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity,
respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.
res.in:5500/) was developed using flask API, along with its mobile app. This
server/App will provide researchers a convenient tool, which is rapid and
economical in identification of proteins for abiotic stress management in
crops Poaceae family, in endeavour of higher production for food security
and combating hunger, ensuring UN SDG goal 2.0.