Weighted-persistent-homology-based machine learning for RNA flexibility analysis

With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, Debye-Waller factor, also known as B-factor, measures atomic mean-square displacement and is usually considered as an i...

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Main Authors: Pun, Chi Seng, Yong, Brandon Yung Sin, Xia, Kelin
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/147087
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1470872023-02-28T19:53:33Z Weighted-persistent-homology-based machine learning for RNA flexibility analysis Pun, Chi Seng Yong, Brandon Yung Sin Xia, Kelin School of Physical and Mathematical Sciences School of Biological Sciences Science::Biological sciences Machine Learning Artificial Neural Networks With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, Debye-Waller factor, also known as B-factor, measures atomic mean-square displacement and is usually considered as an important measurement for flexibility. Theoretically, elastic network models, Gaussian network model, flexibility-rigidity model, and other computational models have been proposed for flexibility analysis by shedding light on the biomolecular inner topological structures. Recently, a topology-based machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein B-factor prediction. Motivated by its success, we propose weighted-persistent-homology (WPH)-based machine learning (WPHML) models for RNA flexibility analysis. Our WPH is a newly-proposed model, which incorporate physical, chemical and biological information into topological measurements using a weight function. In particular, we use local persistent homology (LPH) to focus on the topological information of local regions. Our WPHML model is validated on a well-established RNA dataset, and numerical experiments show that our model can achieve a PCC of up to 0.5822. The comparison with the previous sequence-information-based learning models shows that a consistent improvement in performance by at least 10% is achieved in our current model. Ministry of Education (MOE) Nanyang Technological University Published version This research is supported by Nanyang Technological University Startup Grants M4081840 and M4081842 to CSP, Data Science and Artificial Intelligence Research Centre@NTU M4082115 to CSP, and Singapore Ministry of Education Academic Research Fund Tier 1 RG31/18, RG109/ 19, and Tier 2 MOE2018-T2-1-033 to KX. There was no additional external funding received for this study. 2021-03-26T03:42:04Z 2021-03-26T03:42:04Z 2020 Journal Article Pun, C. S., Yong, B. Y. S. & Xia, K. (2020). Weighted-persistent-homology-based machine learning for RNA flexibility analysis. PloS One, 15(8). https://dx.doi.org/10.1371/journal.pone.0237747 1932-6203 https://hdl.handle.net/10356/147087 10.1371/journal.pone.0237747 32822369 2-s2.0-85089807051 8 15 en M4081840 M4081842 M4082115 RG31/18 RG109/19 MOE2018-T2-1-033 PloS One © 2020 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Biological sciences
Machine Learning
Artificial Neural Networks
spellingShingle Science::Biological sciences
Machine Learning
Artificial Neural Networks
Pun, Chi Seng
Yong, Brandon Yung Sin
Xia, Kelin
Weighted-persistent-homology-based machine learning for RNA flexibility analysis
description With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, Debye-Waller factor, also known as B-factor, measures atomic mean-square displacement and is usually considered as an important measurement for flexibility. Theoretically, elastic network models, Gaussian network model, flexibility-rigidity model, and other computational models have been proposed for flexibility analysis by shedding light on the biomolecular inner topological structures. Recently, a topology-based machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein B-factor prediction. Motivated by its success, we propose weighted-persistent-homology (WPH)-based machine learning (WPHML) models for RNA flexibility analysis. Our WPH is a newly-proposed model, which incorporate physical, chemical and biological information into topological measurements using a weight function. In particular, we use local persistent homology (LPH) to focus on the topological information of local regions. Our WPHML model is validated on a well-established RNA dataset, and numerical experiments show that our model can achieve a PCC of up to 0.5822. The comparison with the previous sequence-information-based learning models shows that a consistent improvement in performance by at least 10% is achieved in our current model.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Pun, Chi Seng
Yong, Brandon Yung Sin
Xia, Kelin
format Article
author Pun, Chi Seng
Yong, Brandon Yung Sin
Xia, Kelin
author_sort Pun, Chi Seng
title Weighted-persistent-homology-based machine learning for RNA flexibility analysis
title_short Weighted-persistent-homology-based machine learning for RNA flexibility analysis
title_full Weighted-persistent-homology-based machine learning for RNA flexibility analysis
title_fullStr Weighted-persistent-homology-based machine learning for RNA flexibility analysis
title_full_unstemmed Weighted-persistent-homology-based machine learning for RNA flexibility analysis
title_sort weighted-persistent-homology-based machine learning for rna flexibility analysis
publishDate 2021
url https://hdl.handle.net/10356/147087
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