Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2

Ankitha Giridhar; Niranjana Sampathila

doi:10.1007/978-3-030-82469-3_8

Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2

Ankitha Giridhar^*
, Niranjana Sampathila

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

The Covid19 pandemic has impacted the entire world negatively, and scientists, healthcare professionals and engineers are all on the search for viable solutions. During the search for a vaccine for the virus, scientifically known as SARS-CoV-2, it was identified as an mRNA virus, which is why mRNA vaccines could be potential solutions. However, mRNA vaccines easily degrade, and the objective of this study was to predict the degradation rates of various potential mRNA strands to potentially select an ideal sequence for a vaccine. This paper details an approach that uses a Neural Network model with the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.

Original language	English
Title of host publication	Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021
Editors	Rajiv Misra, Rudrapatna K. Shyamasundar, Amrita Chaturvedi, Rana Omer
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	85-94
Number of pages	10
ISBN (Print)	9783030824686
DOIs	https://doi.org/10.1007/978-3-030-82469-3_8
Publication status	Published - 2022
Event	International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021 - Virtual, Online Duration: 29-03-2021 → 30-03-2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	256
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021
City	Virtual, Online
Period	29-03-21 → 30-03-21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-030-82469-3_8

Cite this

Giridhar, A., & Sampathila, N. (2022). Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2. In R. Misra, R. K. Shyamasundar, A. Chaturvedi, & R. Omer (Eds.), Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021 (pp. 85-94). (Lecture Notes in Networks and Systems; Vol. 256). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82469-3_8

Giridhar, Ankitha ; Sampathila, Niranjana. / Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2. Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021. editor / Rajiv Misra ; Rudrapatna K. Shyamasundar ; Amrita Chaturvedi ; Rana Omer. Springer Science and Business Media Deutschland GmbH, 2022. pp. 85-94 (Lecture Notes in Networks and Systems).

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abstract = "The Covid19 pandemic has impacted the entire world negatively, and scientists, healthcare professionals and engineers are all on the search for viable solutions. During the search for a vaccine for the virus, scientifically known as SARS-CoV-2, it was identified as an mRNA virus, which is why mRNA vaccines could be potential solutions. However, mRNA vaccines easily degrade, and the objective of this study was to predict the degradation rates of various potential mRNA strands to potentially select an ideal sequence for a vaccine. This paper details an approach that uses a Neural Network model with the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.",

author = "Ankitha Giridhar and Niranjana Sampathila",

note = "Funding Information: Acknowledgements. The authors would like to thank the Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education for the support in enabling this study. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021 ; Conference date: 29-03-2021 Through 30-03-2021",

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Giridhar, A & Sampathila, N 2022, Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2. in R Misra, RK Shyamasundar, A Chaturvedi & R Omer (eds), Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021. Lecture Notes in Networks and Systems, vol. 256, Springer Science and Business Media Deutschland GmbH, pp. 85-94, International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021, Virtual, Online, 29-03-21. https://doi.org/10.1007/978-3-030-82469-3_8

Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2. / Giridhar, Ankitha; Sampathila, Niranjana.
Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021. ed. / Rajiv Misra; Rudrapatna K. Shyamasundar; Amrita Chaturvedi; Rana Omer. Springer Science and Business Media Deutschland GmbH, 2022. p. 85-94 (Lecture Notes in Networks and Systems; Vol. 256).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2

AU - Giridhar, Ankitha

AU - Sampathila, Niranjana

N1 - Funding Information: Acknowledgements. The authors would like to thank the Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education for the support in enabling this study. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - The Covid19 pandemic has impacted the entire world negatively, and scientists, healthcare professionals and engineers are all on the search for viable solutions. During the search for a vaccine for the virus, scientifically known as SARS-CoV-2, it was identified as an mRNA virus, which is why mRNA vaccines could be potential solutions. However, mRNA vaccines easily degrade, and the objective of this study was to predict the degradation rates of various potential mRNA strands to potentially select an ideal sequence for a vaccine. This paper details an approach that uses a Neural Network model with the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.

AB - The Covid19 pandemic has impacted the entire world negatively, and scientists, healthcare professionals and engineers are all on the search for viable solutions. During the search for a vaccine for the virus, scientifically known as SARS-CoV-2, it was identified as an mRNA virus, which is why mRNA vaccines could be potential solutions. However, mRNA vaccines easily degrade, and the objective of this study was to predict the degradation rates of various potential mRNA strands to potentially select an ideal sequence for a vaccine. This paper details an approach that uses a Neural Network model with the LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) architectures to predict the degradation of each sequence in the given data, which comprised of sequences of mRNA. The performance of the model was evaluated using the MCRMSE (Mean Columnwise Root Mean Squared Error) as the scoring metric.

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T3 - Lecture Notes in Networks and Systems

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BT - Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021

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A2 - Chaturvedi, Amrita

A2 - Omer, Rana

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021

Y2 - 29 March 2021 through 30 March 2021

ER -

Giridhar A, Sampathila N. Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2. In Misra R, Shyamasundar RK, Chaturvedi A, Omer R, editors, Machine Learning and Big Data Analytics - Proceedings of International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 85-94. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-82469-3_8

Application of Artificial Intelligence to Predict the Degradation of Potential mRNA Vaccines Developed to Treat SARS-CoV-2

Abstract

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UN SDGs

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