Abstract
Long non-coding RNAs (lncRNAs) are the molecules of RNA which are greater than 200 nucleotides. They play a major role in pathological processes and the main role in cellular transcripts. In many viral infections, the lncRNAs are involved in viral gene expression enhancement, viral transformation, viral replication and so on. They actually cause many human diseases due to their alterations. The recent research of COVID-19 says that lncRNAs play important role in the replication cycle of “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” by up-regulated and down-regulated protein interactions. The life activities and the disease analysis at the molecular level can be easily analysed by identifying the lncRNAs of a particular disease. The new dimension to the architecture of molecular is added by the lncRNA, which gives a new pathway of opportunities for treatment and also help to know the actual cause of death by knowing the lncRNAs biological functions in the development of the disease. The main problem is identifying the lncRNAs from the huge number of RNA sequences accurately and quickly with sequence technologies is very difficult. Hence the computational identification of COVID-19 lncRNAs-protein interaction is urgently required. The COVID-19 lncRNAs can be easily identified by the recent advancement in computational prediction technology, i.e. machine learning and deep learning approaches which are branches of artificial intelligence (AI). This is a powerful tool to fight against the COVID-19 pandemic. This paper describes how the machine learning and deep learning methods can be used to identify the lncRNAs of COVID-19.
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Revathi, A., Jyothi, S., Swathi, P. (2021). Identification of Coronavirus (COVID-19) Long Non-coding RNAs (lncRNAs) Using Machine and Deep Learning Approaches. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_6
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