A review of computational approaches to predict gene functions
Recently, novel high-throughput biotechnologies have provided rich data about different genomes. However, manual annotation of gene function is time consuming. It is also very expensive and infeasible for the growing amounts of data. At present there are numerous functions in certain species that re...
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Betham Science
2018
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| Online Access: | http://eprints.uthm.edu.my/4705/1/AJ%202018%20%28448%29.pdf http://eprints.uthm.edu.my/4705/ https://doi.org/10.2174/1574893612666171002113742 |
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my.uthm.eprints.47052021-12-14T08:34:51Z http://eprints.uthm.edu.my/4705/ A review of computational approaches to predict gene functions Swee, Kuan Loh Swee, Thing Low Lian, En Chai Weng, Howe Chan Mohamad, Mohd Saberi Deris, Safaai Ibrahim, Zuwairie Kasim, Shahreen Ali Shah, Zuraini Mohd Jamil, Hamimah Zakaria, Zalmiyah Napis, Suhaimi R Medicine (General) T Technology (General) QA299.6-433 Analysis Recently, novel high-throughput biotechnologies have provided rich data about different genomes. However, manual annotation of gene function is time consuming. It is also very expensive and infeasible for the growing amounts of data. At present there are numerous functions in certain species that remain unknown or only partially known. Hence, the use of computational approaches to predicting gene function is becoming widespread. Computational approaches are time saving and less costly. Prediction analysis provided can be used in hypotheses to drive the biological validation of gene function. Objective: This paper reviews computational approaches such as the support vector machine, clustering, hierarchical ensemble and network-based approaches. Methods: Comparisons between these approaches are also made in the discussion portion. Results: In addition, the advantages and disadvantages of these computational approaches are discussed. Conclusion: With the emergence of omics data, the focus should be continued on integrating newly added data for gene functions prediction field. Betham Science 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4705/1/AJ%202018%20%28448%29.pdf Swee, Kuan Loh and Swee, Thing Low and Lian, En Chai and Weng, Howe Chan and Mohamad, Mohd Saberi and Deris, Safaai and Ibrahim, Zuwairie and Kasim, Shahreen and Ali Shah, Zuraini and Mohd Jamil, Hamimah and Zakaria, Zalmiyah and Napis, Suhaimi (2018) A review of computational approaches to predict gene functions. Current Bioinformatics, 13 (4). pp. 373-386. ISSN 1574-8936 https://doi.org/10.2174/1574893612666171002113742 |
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R Medicine (General) T Technology (General) QA299.6-433 Analysis |
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R Medicine (General) T Technology (General) QA299.6-433 Analysis Swee, Kuan Loh Swee, Thing Low Lian, En Chai Weng, Howe Chan Mohamad, Mohd Saberi Deris, Safaai Ibrahim, Zuwairie Kasim, Shahreen Ali Shah, Zuraini Mohd Jamil, Hamimah Zakaria, Zalmiyah Napis, Suhaimi A review of computational approaches to predict gene functions |
| description |
Recently, novel high-throughput biotechnologies have provided rich data about different genomes. However, manual annotation of gene function is time consuming. It is also very expensive and infeasible for the growing amounts of data. At present there are numerous functions in certain species that remain unknown or only partially known. Hence, the use of computational approaches to predicting gene function is becoming widespread. Computational approaches are time saving and less costly. Prediction analysis provided can be used in hypotheses to drive the biological validation of gene function. Objective: This paper reviews computational approaches such as the support vector machine, clustering, hierarchical ensemble and network-based approaches. Methods: Comparisons between these approaches are also made in the discussion portion. Results: In addition, the advantages and disadvantages of these computational approaches are discussed. Conclusion: With the emergence of omics data, the focus should be continued on integrating newly added data for gene functions prediction field. |
| format |
Article |
| author |
Swee, Kuan Loh Swee, Thing Low Lian, En Chai Weng, Howe Chan Mohamad, Mohd Saberi Deris, Safaai Ibrahim, Zuwairie Kasim, Shahreen Ali Shah, Zuraini Mohd Jamil, Hamimah Zakaria, Zalmiyah Napis, Suhaimi |
| author_facet |
Swee, Kuan Loh Swee, Thing Low Lian, En Chai Weng, Howe Chan Mohamad, Mohd Saberi Deris, Safaai Ibrahim, Zuwairie Kasim, Shahreen Ali Shah, Zuraini Mohd Jamil, Hamimah Zakaria, Zalmiyah Napis, Suhaimi |
| author_sort |
Swee, Kuan Loh |
| title |
A review of computational approaches to predict gene functions |
| title_short |
A review of computational approaches to predict gene functions |
| title_full |
A review of computational approaches to predict gene functions |
| title_fullStr |
A review of computational approaches to predict gene functions |
| title_full_unstemmed |
A review of computational approaches to predict gene functions |
| title_sort |
review of computational approaches to predict gene functions |
| publisher |
Betham Science |
| publishDate |
2018 |
| url |
http://eprints.uthm.edu.my/4705/1/AJ%202018%20%28448%29.pdf http://eprints.uthm.edu.my/4705/ https://doi.org/10.2174/1574893612666171002113742 |
| _version_ |
1738581287864631296 |
| score |
13.211869 |