Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148523
Title: Data-driven learning models for protein folding analysis
Authors: Tedja, Erika
Keywords: Science::Mathematics
Issue Date: 2021
Publisher: Nanyang Technological University
Source: 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
Abstract: 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.
URI: https://hdl.handle.net/10356/148523
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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