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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Main Author: Seah, Lorraine Xuan Hui
Other Authors: Xia Kelin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/166442
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics
spellingShingle Science::Mathematics
Seah, Lorraine Xuan Hui
Topology based learning models for SARS-CoV-2 mutation analysis
description 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
author_facet Xia Kelin
Seah, Lorraine Xuan Hui
format Final Year Project
author Seah, Lorraine Xuan Hui
author_sort 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
title_full_unstemmed Topology based learning models for SARS-CoV-2 mutation analysis
title_sort topology based learning models for sars-cov-2 mutation analysis
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166442
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