Bayesian alignment of continuous molecular shapes using random fields
Statistical methodology is proposed for comparing molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Baye...
| Main Authors: | , , |
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| Format: | Article |
| Published: |
2008
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| Online Access: | https://eprints.nottingham.ac.uk/919/ |
| _version_ | 1848790503150059520 |
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| author | Czogiel, Irina Dryden, Ian L. Brignell, Christopher J. |
| author_facet | Czogiel, Irina Dryden, Ian L. Brignell, Christopher J. |
| author_sort | Czogiel, Irina |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Statistical methodology is proposed for comparing
molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Bayesian inference is carried out using Markov chain Monte Carlo methods for the pairwise alignment of the resulting molecular fields. Superimposing entire fields rather than the configuration matrices of nuclear positions thereby solves the problem that there is usually no clear one--to--one correspondence between the atoms of the two molecules under consideration. Using a similar concept, we also propose an adaptation of the generalised Procrustes analysis algorithm for the simultaneous alignment of multiple molecular fields. The methodology is applied to a dataset of 31 steroid molecules. |
| first_indexed | 2025-11-14T18:13:39Z |
| format | Article |
| id | nottingham-919 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:13:39Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-9192020-05-04T20:27:39Z https://eprints.nottingham.ac.uk/919/ Bayesian alignment of continuous molecular shapes using random fields Czogiel, Irina Dryden, Ian L. Brignell, Christopher J. Statistical methodology is proposed for comparing molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Bayesian inference is carried out using Markov chain Monte Carlo methods for the pairwise alignment of the resulting molecular fields. Superimposing entire fields rather than the configuration matrices of nuclear positions thereby solves the problem that there is usually no clear one--to--one correspondence between the atoms of the two molecules under consideration. Using a similar concept, we also propose an adaptation of the generalised Procrustes analysis algorithm for the simultaneous alignment of multiple molecular fields. The methodology is applied to a dataset of 31 steroid molecules. 2008 Article NonPeerReviewed Czogiel, Irina, Dryden, Ian L. and Brignell, Christopher J. (2008) Bayesian alignment of continuous molecular shapes using random fields. .. . (Submitted) |
| spellingShingle | Czogiel, Irina Dryden, Ian L. Brignell, Christopher J. Bayesian alignment of continuous molecular shapes using random fields |
| title | Bayesian alignment of continuous molecular shapes using random fields |
| title_full | Bayesian alignment of continuous molecular shapes using random fields |
| title_fullStr | Bayesian alignment of continuous molecular shapes using random fields |
| title_full_unstemmed | Bayesian alignment of continuous molecular shapes using random fields |
| title_short | Bayesian alignment of continuous molecular shapes using random fields |
| title_sort | bayesian alignment of continuous molecular shapes using random fields |
| url | https://eprints.nottingham.ac.uk/919/ |