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A Review of Computational Approaches to Predict Gene Functions

[ Vol. 13 , Issue. 4 ]

Author(s):

Swee Kuan Loh, Swee Thing Low, Lian En Chai, Weng Howe Chan, Mohd Saberi Mohamad*, Safaai Deris, Zuwairie Ibrahim, Shahreen Kasim, Zuraini Ali Shah, Hamimah Mohd Jamil, Zalmiyah Zakaria and Suhaimi Napis*   Pages 373 - 386 ( 14 )

Abstract:


Background: 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.

Keywords:

Artificial intelligence, gene function, functional prediction, classifier, computational biology.

Affiliation:

Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Institute For Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, 86400, Parit Raja, Batu Pahat, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor

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