Data-driven learning models for protein folding analysis

In this paper, the protein folding prediction problem is modelled and solved using reinforcement learning. Deep learning methods have been commonly used to predict protein structure and folding in recent years. However, it has been found that this method may not take into account the energy function...

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Main Author: Tedja, Erika
Other Authors: Xia Kelin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148523
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1485232023-02-28T23:13:42Z Data-driven learning models for protein folding analysis Tedja, Erika Xia Kelin School of Physical and Mathematical Sciences xiakelin@ntu.edu.sg Science::Mathematics In this paper, the protein folding prediction problem is modelled and solved using reinforcement learning. Deep learning methods have been commonly used to predict protein structure and folding in recent years. However, it has been found that this method may not take into account the energy function and hydrophobic-polar nature of proteins. This paper attempted to use the Q-learning approach using ℇ-greedy policy to predict the secondary structure of protein given its amino acid sequence and optimal energy. This paper can be improved by incorporating pretraining of the agent or using more advanced deep Q-learning algorithm. Bachelor of Science in Mathematical Sciences and Economics 2021-04-29T03:07:55Z 2021-04-29T03:07:55Z 2021 Final Year Project (FYP) Tedja, E. (2021). Data-driven learning models for protein folding analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148523 https://hdl.handle.net/10356/148523 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
Tedja, Erika
Data-driven learning models for protein folding analysis
description In this paper, the protein folding prediction problem is modelled and solved using reinforcement learning. Deep learning methods have been commonly used to predict protein structure and folding in recent years. However, it has been found that this method may not take into account the energy function and hydrophobic-polar nature of proteins. This paper attempted to use the Q-learning approach using ℇ-greedy policy to predict the secondary structure of protein given its amino acid sequence and optimal energy. This paper can be improved by incorporating pretraining of the agent or using more advanced deep Q-learning algorithm.
author2 Xia Kelin
author_facet Xia Kelin
Tedja, Erika
format Final Year Project
author Tedja, Erika
author_sort Tedja, Erika
title Data-driven learning models for protein folding analysis
title_short Data-driven learning models for protein folding analysis
title_full Data-driven learning models for protein folding analysis
title_fullStr Data-driven learning models for protein folding analysis
title_full_unstemmed Data-driven learning models for protein folding analysis
title_sort data-driven learning models for protein folding analysis
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148523
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