Topology based learning models for SARS-CoV-2 mutation analysis
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a global pandemic after its first appearance in December 2019 remains circulating in our society. Since then, many mutations had emerged such as Alpha, Beta, Gamma, Delta, Omicron and many other mutations even with the discover...
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sg-ntu-dr.10356-1664422023-05-01T15:35:45Z Topology based learning models for SARS-CoV-2 mutation analysis Seah, Lorraine Xuan Hui Xia Kelin School of Physical and Mathematical Sciences xiakelin@ntu.edu.sg Science::Mathematics Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a global pandemic after its first appearance in December 2019 remains circulating in our society. Since then, many mutations had emerged such as Alpha, Beta, Gamma, Delta, Omicron and many other mutations even with the discovery of vaccinations. Therefore, the possibility of a mutation of higher infectivity appearing exists as long as the virus remains in circulation. The ability to predict infectivity of possible mutations remains crucial in protecting lives. Infectivity is measured by the interaction between the receptor-binding domain (RBD) on the S-protein of SARS-CoV-2 (antibody) and the Angiotensin-Converting Enzyme 2 (ACE2) on human cells(antigen). Both of which are proteins and can be generally classified as protein-protein interactions in an antigen-antibody complex. Topological descriptors generated by persistent homology captures the intrinsic biological information of protein-protein interactions upon mutation through the extraction of essential features from high-dimensional dataset. Machine learning model employed like the Gradient Boosting Tree (GBT) incorporates topological descriptors to predict changes in binding affinity upon mutations. The changes in binding affinity indicates if the mutation has strengthened in its infectivity. Bachelor of Science in Mathematical Sciences and Economics 2023-04-28T07:48:32Z 2023-04-28T07:48:32Z 2023 Final Year Project (FYP) Seah, L. X. H. (2023). Topology based learning models for SARS-CoV-2 mutation analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166442 https://hdl.handle.net/10356/166442 en application/pdf Nanyang Technological University |
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Science::Mathematics Seah, Lorraine Xuan Hui Topology based learning models for SARS-CoV-2 mutation analysis |
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a global pandemic after its first appearance in December 2019 remains circulating in our society. Since then, many mutations had emerged such as Alpha, Beta, Gamma, Delta, Omicron and many other mutations even with the discovery of vaccinations. Therefore, the possibility of a mutation of higher infectivity appearing exists as long as the virus remains in circulation. The ability to predict infectivity of possible mutations remains crucial in protecting lives.
Infectivity is measured by the interaction between the receptor-binding domain (RBD) on the S-protein of SARS-CoV-2 (antibody) and the Angiotensin-Converting Enzyme 2 (ACE2) on human cells(antigen). Both of which are proteins and can be generally classified as protein-protein interactions in an antigen-antibody complex.
Topological descriptors generated by persistent homology captures the intrinsic biological information of protein-protein interactions upon mutation through the extraction of essential features from high-dimensional dataset.
Machine learning model employed like the Gradient Boosting Tree (GBT) incorporates topological descriptors to predict changes in binding affinity upon mutations. The changes in binding affinity indicates if the mutation has strengthened in its infectivity. |
author2 |
Xia Kelin |
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Xia Kelin Seah, Lorraine Xuan Hui |
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Final Year Project |
author |
Seah, Lorraine Xuan Hui |
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Seah, Lorraine Xuan Hui |
title |
Topology based learning models for SARS-CoV-2 mutation analysis |
title_short |
Topology based learning models for SARS-CoV-2 mutation analysis |
title_full |
Topology based learning models for SARS-CoV-2 mutation analysis |
title_fullStr |
Topology based learning models for SARS-CoV-2 mutation analysis |
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Topology based learning models for SARS-CoV-2 mutation analysis |
title_sort |
topology based learning models for sars-cov-2 mutation analysis |
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Nanyang Technological University |
publishDate |
2023 |
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https://hdl.handle.net/10356/166442 |
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