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Comparative analysis of machine learning based classification for abiotic stress proteins

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Title Comparative analysis of machine learning based classification for abiotic stress proteins
Not Available
 
Creator Bulbul Ahmed
Anil Rai
Mir Asif Iquebal
Sarika Jaiswal
 
Subject Classification
Deep learning
LSTM
Poaceae
Random forest
SVM
 
Description Not Available
For thousands of years, cereals which include rice, wheat, maize, sorghum and millets etc. have been playing major
role in human civilization. These are the principal components of human diet and important staples for daily survival of
billions of people globally. The cereal crops belong to poaceae family and rich in vitamins, minerals and fiber. They are
reported to reduce the coronary heart disease and other serious diseases. These crops are adversely affected by biotic
and abiotic stresses like cold, drought, heat and salinity. With the advent of modern NGS technologies, the plethora of
molecular data leads to infer many unexplored facts of the cereal crops using in-silico approach. In the present work,
computational techniques were applied to study thoroughly the classification of abiotic stresses (cold, drought, heat
and salinity) responsive genes in cereals. The datasets of four stress responsive genes in poaceae family was retrieved
from public domain. The machine learning based methodologies namely, Random forest, Support Vector Machines
and Deep Learning-Long Short-Term Memory (DL-LSTM) were applied. A comparative analysis was carried out for
classification of the retrieved data with k-fold cross validation applying the machine learning techniques at different
parameters. It was observed that for all the four sets of data, accuracy was maximum, i.e. 95.11%, 76.88%, 94.31%
and 82.04% for cold, drought, heat and salinity, respectively using DL-LSTM. Comparison of the methodologies
obviates the outperformance of deep leaning. Such approach of computational studies will help researchers to study
the complex biological problems of gene classification more efficiently.
Not Available
 
Date 2022-03-19T09:45:54Z
2022-03-19T09:45:54Z
2021-06-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/70372
 
Language English
 
Relation Not Available;
 
Publisher Not Available