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Computational aspects of differential networks
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613480067006465 |
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| access_status_str | Open Access |
| author2 | Arashi, Mohammad |
| author_browse | Arashi, Mohammad |
| author_facet | Arashi, Mohammad |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83074 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:48.836Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/83074 Computational aspects of differential networks Arashi, Mohammad u16077131@tuks.co.za Bekker, Andriette, 1958- Marques Salgado, Ricardo Daniel Differential networks Computational statistics UCTD Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. The need for statistical tools capable of addressing high-dimensional data is ever-growing. One such tool is that of differential networks, which have become increasing popular within various branches of science. The popularity of differential networks and their subsequent analysis is largely attributed to their ability to effectively represent the relationships between factors of complex systems over time, or over various experimental conditions. However, a differential network is not easily calculated, and in high dimensional settings common within biological sciences they must be estimated.Motivated by this, this dissertation comprehensively explores differential networks and the efficient estimation thereof through the use of a R package developed throughout the course of this research- dineR. The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged (SRUG190308422768 Grant Number 120839). Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. Statistics MSc (Advanced Data Analytics) Unrestricted 2021-12-15T13:50:00Z 2021-12-15T13:50:00Z 2022 2021 Mini Dissertation * A2022 http://hdl.handle.net/2263/83074 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Differential networks Computational statistics UCTD Computational aspects of differential networks |
| title | Computational aspects of differential networks |
| title_full | Computational aspects of differential networks |
| title_fullStr | Computational aspects of differential networks |
| title_full_unstemmed | Computational aspects of differential networks |
| title_short | Computational aspects of differential networks |
| title_sort | computational aspects of differential networks |
| topic | Differential networks Computational statistics UCTD |
| url | http://hdl.handle.net/2263/83074 |