COVID-19 mRNA Vaccine Degradation Prediction By Using Deep Learning Algorithms Article Swipe
Ooi Nian Chze
,
Azian Azamimi Abdullah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/3ict56508.2022.9990617
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/3ict56508.2022.9990617
The worldwide coronavirus (COVID-19) pandemic has accelerated substantially in the 2020, necessitating a global collaborative from various entities to create and speed vaccine development to prevent illnesses and deaths. Because of its fast development, high efficiently, safe administration, and low-cost production, messenger RNA (mRNA) has emerged as a significant technology in this epidemic. However, due of the inadequate in vivo distribution of mRNA, its chemical qualities make it difficult to use the vaccine. As a result, the goal of this study is to create and construct a sequence deep model that will be used to predict the degradation rate of the COVID-19 mRNA vaccine using five reactivity values for each place in the mRNA sequence. The probability degradation rate with/without magnesium at pH10 and 50°C was one of four of these values. The fifth reactivity value shows the likelihood of the RNA sample's secondary structure. The numerical and categorical properties of the deep learning model are the most important. Categorical features are referred from the structures, sequences, and predicted loop of the mRNA sequence, while numerical features are extracted via mathematical computations. 6 models of bidirectional layers models (LSTM, GRU, LSTM+GRU (L_GRU), GRU+LSTM (G_LSTM), LSTM+GRU+LSTM (L_G_LSTM), and GRU+LSTM+GRU (G_L_GRU) give trustworthy projected outcomes because it comprises five reactivity values and validate by mean columnwise root mean square error (MCRMSE). The MCRMSE results are then used to evaluate the performance. The stronger the prediction model, the smaller the values are. The best-fitting model is L_G_LSTM with the MCRMSE difference of 0.007 will be implemented into a Graphical User Interface (GUI) prediction system.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/3ict56508.2022.9990617
- https://ieeexplore.ieee.org/ielx7/9989532/9990057/09990617.pdf
- OA Status
- bronze
- Cited By
- 4
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313338978
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313338978Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/3ict56508.2022.9990617Digital Object Identifier
- Title
-
COVID-19 mRNA Vaccine Degradation Prediction By Using Deep Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-20Full publication date if available
- Authors
-
Ooi Nian Chze, Azian Azamimi AbdullahList of authors in order
- Landing page
-
https://doi.org/10.1109/3ict56508.2022.9990617Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/9989532/9990057/09990617.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/9989532/9990057/09990617.pdfDirect OA link when available
- Concepts
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Categorical variable, Artificial intelligence, Deep learning, Algorithm, Computer science, Sequence (biology), Machine learning, Metric (unit), Coronavirus disease 2019 (COVID-19), Medicine, Engineering, Biology, Disease, Genetics, Pathology, Infectious disease (medical specialty), Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 80 |
| abstract_inverted_index.these | 130 |
| abstract_inverted_index.using | 104 |
| abstract_inverted_index.value | 135 |
| abstract_inverted_index.while | 174 |
| abstract_inverted_index.(LSTM, | 188 |
| abstract_inverted_index.(mRNA) | 43 |
| abstract_inverted_index.MCRMSE | 220, 246 |
| abstract_inverted_index.create | 19, 83 |
| abstract_inverted_index.global | 13 |
| abstract_inverted_index.layers | 186 |
| abstract_inverted_index.model, | 233 |
| abstract_inverted_index.models | 183, 187 |
| abstract_inverted_index.square | 216 |
| abstract_inverted_index.values | 107, 208, 237 |
| abstract_inverted_index.Because | 29 |
| abstract_inverted_index.because | 203 |
| abstract_inverted_index.deaths. | 28 |
| abstract_inverted_index.emerged | 45 |
| abstract_inverted_index.predict | 95 |
| abstract_inverted_index.prevent | 25 |
| abstract_inverted_index.result, | 75 |
| abstract_inverted_index.results | 221 |
| abstract_inverted_index.smaller | 235 |
| abstract_inverted_index.system. | 260 |
| abstract_inverted_index.vaccine | 22, 103 |
| abstract_inverted_index.values. | 131 |
| abstract_inverted_index.various | 16 |
| abstract_inverted_index.(L_GRU), | 191 |
| abstract_inverted_index.COVID-19 | 101 |
| abstract_inverted_index.GRU+LSTM | 192 |
| abstract_inverted_index.However, | 53 |
| abstract_inverted_index.LSTM+GRU | 190 |
| abstract_inverted_index.L_G_LSTM | 243 |
| abstract_inverted_index.chemical | 64 |
| abstract_inverted_index.entities | 17 |
| abstract_inverted_index.evaluate | 226 |
| abstract_inverted_index.features | 160, 176 |
| abstract_inverted_index.learning | 153 |
| abstract_inverted_index.low-cost | 39 |
| abstract_inverted_index.outcomes | 202 |
| abstract_inverted_index.pandemic | 4 |
| abstract_inverted_index.referred | 162 |
| abstract_inverted_index.sample's | 142 |
| abstract_inverted_index.sequence | 87 |
| abstract_inverted_index.stronger | 230 |
| abstract_inverted_index.vaccine. | 72 |
| abstract_inverted_index.validate | 210 |
| abstract_inverted_index.(G_LSTM), | 193 |
| abstract_inverted_index.(G_L_GRU) | 198 |
| abstract_inverted_index.(MCRMSE). | 218 |
| abstract_inverted_index.Graphical | 255 |
| abstract_inverted_index.Interface | 257 |
| abstract_inverted_index.comprises | 205 |
| abstract_inverted_index.construct | 85 |
| abstract_inverted_index.difficult | 68 |
| abstract_inverted_index.epidemic. | 52 |
| abstract_inverted_index.extracted | 178 |
| abstract_inverted_index.illnesses | 26 |
| abstract_inverted_index.magnesium | 120 |
| abstract_inverted_index.messenger | 41 |
| abstract_inverted_index.numerical | 146, 175 |
| abstract_inverted_index.predicted | 168 |
| abstract_inverted_index.projected | 201 |
| abstract_inverted_index.qualities | 65 |
| abstract_inverted_index.secondary | 143 |
| abstract_inverted_index.sequence, | 173 |
| abstract_inverted_index.sequence. | 114 |
| abstract_inverted_index.worldwide | 1 |
| abstract_inverted_index.(COVID-19) | 3 |
| abstract_inverted_index.columnwise | 213 |
| abstract_inverted_index.difference | 247 |
| abstract_inverted_index.important. | 158 |
| abstract_inverted_index.inadequate | 57 |
| abstract_inverted_index.likelihood | 138 |
| abstract_inverted_index.prediction | 232, 259 |
| abstract_inverted_index.properties | 149 |
| abstract_inverted_index.reactivity | 106, 134, 207 |
| abstract_inverted_index.sequences, | 166 |
| abstract_inverted_index.structure. | 144 |
| abstract_inverted_index.technology | 49 |
| abstract_inverted_index.(L_G_LSTM), | 195 |
| abstract_inverted_index.Categorical | 159 |
| abstract_inverted_index.accelerated | 6 |
| abstract_inverted_index.categorical | 148 |
| abstract_inverted_index.coronavirus | 2 |
| abstract_inverted_index.degradation | 97, 117 |
| abstract_inverted_index.development | 23 |
| abstract_inverted_index.implemented | 252 |
| abstract_inverted_index.probability | 116 |
| abstract_inverted_index.production, | 40 |
| abstract_inverted_index.significant | 48 |
| abstract_inverted_index.structures, | 165 |
| abstract_inverted_index.trustworthy | 200 |
| abstract_inverted_index.GRU+LSTM+GRU | 197 |
| abstract_inverted_index.best-fitting | 240 |
| abstract_inverted_index.development, | 33 |
| abstract_inverted_index.distribution | 60 |
| abstract_inverted_index.efficiently, | 35 |
| abstract_inverted_index.mathematical | 180 |
| abstract_inverted_index.performance. | 228 |
| abstract_inverted_index.with/without | 119 |
| abstract_inverted_index.LSTM+GRU+LSTM | 194 |
| abstract_inverted_index.bidirectional | 185 |
| abstract_inverted_index.collaborative | 14 |
| abstract_inverted_index.computations. | 181 |
| abstract_inverted_index.necessitating | 11 |
| abstract_inverted_index.substantially | 7 |
| abstract_inverted_index.administration, | 37 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.98265896 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |