Next Generation Sequence Prediction Intelligent System for SARS-COV-2 Using Deep Learning Neural Network

Mohamed, Takwa; Sayed, Sabah; Salah, Akram; Houssein, Essam H.

doi:10.1109/icenco49852.2021.9698948

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Artificial Intelligence (AI) models can also learn hidden evolution patterns from the huge number of virus sequences submitted, prioritizing future potential viral mutations that could introduce the next VOCs (Chen et al 2020;Mohamed et al 2021).…”

Section: Overview Of the Problemmentioning

confidence: 99%

“…Computational methods such as the Gillespie algorithms can be used to simulate realistic substitution patterns of closely related genomic large-scale datasets, for example, simulators targeting gene trees, ancestral recombination graphs, or phylogenetic trees (Beiko and Charlebois 2007; Hudson 2002; Laval and Excoffier 2004; Ewing and Hermisson 2010; Rambaut and Grass 1997; Fletcher and Yang 2009; Sipos et al 2011; De Maio et al 2022; Shchur et al 2022). Artificial Intelligence (AI) models can also learn hidden evolution patterns from the huge number of virus sequences submitted, prioritizing future potential viral mutations that could introduce the next VOCs (Chen et al 2020; Mohamed et al 2021).…”

Section: Overview Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants

Chen

Nie

et al. 2023

The International Journal of High Performance Computing Applica

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The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS-CoV-2 spike receptor-binding domain (RBD) mutations and identify high-risk variants. We pretrain a large protein language model with approximately 408 million protein sequences and construct a high-throughput screening for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mutation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9× speedup in mixed-precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theoretical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in order to prepare for a future pandemic that will inevitably take place. Our models are released at https://github.com/ZhiweiNiepku/SARS-CoV-2_mutation_simulation to facilitate future related work.

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Section: Overview Of the Problemmentioning

confidence: 99%

Section: Overview Of the Problemmentioning

confidence: 99%

Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants

Chen

Nie

et al. 2023

The International Journal of High Performance Computing Applica

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“…By analyzing existing genomic data, the researchers aim to build a model that is capable of accurately predicting the genetic sequences of future viral strains. This could provide valuable insights into the evolution and behavior of the virus, aiding in the development of targeted interventions and treatments [27][28][29].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

et al. 2023

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This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission’s stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

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“…Computational methods such as the Gillespie algorithms can be used to simulate realistic substitution patterns of large-scale closely related genomic datasets, e.g., simulators targeting gene trees, ancestral recombination graphs, or phylogenetic trees [24][25][26][27][28][29][30][31][32]. Deep learning (DL) models can also learn hidden evolution patterns from the massive number of submitted virus sequences, thus prioritizing potential future viral mutations that might introduce the next VOCs [33,34].…”

Section: Overview Of the Problemmentioning

confidence: 99%

“…Computational methods such as the Gillespie algorithms can be used to simulate realistic substitution patterns of closely related genomic large-scale datasets, e.g., simulators targeting gene trees, ancestral recombination graphs, or phylogenetic trees (Beiko and Charlebois 2007; Hudson 2002; Laval and Excoffier 2004; Ewing and Hermisson 2010; Rambaut and Grass 1997; Fletcher and Yang 2009; Sipos et al 2011; De Maio et al 2022; Shchur et al 2022). Artificial Intelligence (AI) models can also learn hidden evolution patterns from the huge number of virus sequences submitted, prioritizing future potential viral mutations that could introduce the next VOCs (Chen et al 2020; Mohamed et al 2021).…”

mentioning

confidence: 99%

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

Chen

Nie

et al. 2022

Preprint

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The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS- CoV-2 spike receptor binding domain (RBD) mutations and identify high-risk variants. We pretrain a large pro- tein language model on approximately 408 million pro- tein sequences and construct a high-throughput screen- ing for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mu- tation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9X speedup in mixed precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theo- retical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in or- der to prepare for a future pandemic that will inevitably take place.

show abstract

Next Generation Sequence Prediction Intelligent System for SARS-COV-2 Using Deep Learning Neural Network

Cited by 5 publications

References 18 publications

Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants

Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

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