A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods
Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular rese...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/542824 |
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| _version_ | 1832548404498006016 |
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| author | Jaehee Jung Heung Ki Lee Gangman Yi |
| author_facet | Jaehee Jung Heung Ki Lee Gangman Yi |
| author_sort | Jaehee Jung |
| collection | DOAJ |
| description | Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one. |
| format | Article |
| id | doaj-art-7dcf590ea7eb4762bc749731bb3fd528 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-7dcf590ea7eb4762bc749731bb3fd5282025-02-03T06:14:08ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/542824542824A Novel Method for Functional Annotation Prediction Based on Combination of Classification MethodsJaehee Jung0Heung Ki Lee1Gangman Yi2Samsung Electronics, Suwon, Republic of KoreaSamsung Electronics, Suwon, Republic of KoreaDepartment of Computer Science & Engineering, Gangneung-Wonju National University, Gangwon, Republic of KoreaAutomated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.http://dx.doi.org/10.1155/2014/542824 |
| spellingShingle | Jaehee Jung Heung Ki Lee Gangman Yi A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods The Scientific World Journal |
| title | A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods |
| title_full | A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods |
| title_fullStr | A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods |
| title_full_unstemmed | A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods |
| title_short | A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods |
| title_sort | novel method for functional annotation prediction based on combination of classification methods |
| url | http://dx.doi.org/10.1155/2014/542824 |
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