Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156992
Title: Covid-19 antibody discovery with topology-enhanced learning models
Authors: Wang, Zihong
Keywords: Science::Mathematics
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wang, Z. (2022). Covid-19 antibody discovery with topology-enhanced learning models. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156992
Abstract: The recent global spread of Coronavirus disease 2019 (COVID-19) has been fueled by the appearance of various new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including Alpha, Beta, Gamma, Delta, Omicron, etc. The impact of variants on infectivity and monoclonal antibodies (mAbs) effectiveness is largely determined by how related receptor-binding domain (RBD) mutations affect ACE2 and antibody binding. Topological Data Analysis (TDA) is a fast-growing field combining topology and computational geometry to retrieve features from high-dimensional datasets. In the face of noisy and incomplete datasets, TDA deals with qualitative geometric features independent of metric choice and minimizes the effect of noise. This project reviews antibody structure, function, mutation impact and existing antibodies in clinical trials. We introduce the TopNetTree model which consists of topological feature generation and network models to predict the binding free energy change induced by RBD mutations. The previous related paper about mutation impact is also reviewed and some of the results are validated.
URI: https://hdl.handle.net/10356/156992
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Covid 19 antibody_discovery_with_topology_enhanced_learning_models.pdf
  Restricted Access
1.65 MBAdobe PDFView/Open

Page view(s)

21
Updated on May 20, 2022

Download(s)

3
Updated on May 20, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.