A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods
Joint Authors
Lee, Heung Ki
Yi, Gangman
Jung, Jaehee
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Jung, Jaehee& Lee, Heung Ki& Yi, Gangman. 2014. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1050065
Modern Language Association (MLA)
Jung, Jaehee…[et al.]. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1050065
American Medical Association (AMA)
Jung, Jaehee& Lee, Heung Ki& Yi, Gangman. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1050065
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050065