Review of machine learning algorithms in differential expression analysis
In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases....
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/56285 |
| _version_ | 1848759834460028928 |
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| author | Kuznetsova, I. Karpievitch, Y. Filipovska, A. Lugmayr, Artur Holzinger, A. |
| author_facet | Kuznetsova, I. Karpievitch, Y. Filipovska, A. Lugmayr, Artur Holzinger, A. |
| author_sort | Kuznetsova, I. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNA-Seq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments. |
| first_indexed | 2025-11-14T10:06:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56285 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:11Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-562852017-08-24T02:23:15Z Review of machine learning algorithms in differential expression analysis Kuznetsova, I. Karpievitch, Y. Filipovska, A. Lugmayr, Artur Holzinger, A. In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNA-Seq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments. 2016 Conference Paper http://hdl.handle.net/20.500.11937/56285 restricted |
| spellingShingle | Kuznetsova, I. Karpievitch, Y. Filipovska, A. Lugmayr, Artur Holzinger, A. Review of machine learning algorithms in differential expression analysis |
| title | Review of machine learning algorithms in differential expression analysis |
| title_full | Review of machine learning algorithms in differential expression analysis |
| title_fullStr | Review of machine learning algorithms in differential expression analysis |
| title_full_unstemmed | Review of machine learning algorithms in differential expression analysis |
| title_short | Review of machine learning algorithms in differential expression analysis |
| title_sort | review of machine learning algorithms in differential expression analysis |
| url | http://hdl.handle.net/20.500.11937/56285 |