Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80078
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | You, ZH | - |
dc.creator | Li, S | - |
dc.creator | Gao, X | - |
dc.creator | Luo X | - |
dc.creator | Ji, Z | - |
dc.date.accessioned | 2018-12-21T07:14:52Z | - |
dc.date.available | 2018-12-21T07:14:52Z | - |
dc.identifier.issn | 2314-6133 | - |
dc.identifier.uri | http://hdl.handle.net/10397/80078 | - |
dc.language.iso | en | en_US |
dc.publisher | Hindawi Publishing Corporation | en_US |
dc.rights | Copyright © 2014 Zhu-Hong You et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.rights | The following publication You, Z. -., Li, S., Gao, X., Luo, X., & Ji, Z. (2014). Large-scale protein-protein interactions detection by integrating big biosensing data with computational model. BioMed Research International, 2014, 598129, 1-9 is available at https://dx.doi.org/10.1155/2014/598129 | en_US |
dc.title | Large-scale protein-protein interactions detection by integrating big biosensing data with computational model | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.volume | 2014 | - |
dc.identifier.doi | 10.1155/2014/598129 | - |
dcterms.abstract | Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions.However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | BioMed research international, 2014, v. 2014, 598129, p. 1-9 | - |
dcterms.isPartOf | BioMed research international | - |
dcterms.issued | 2014 | - |
dc.identifier.scopus | 2-s2.0-84907417781 | - |
dc.identifier.pmid | 25215285 | - |
dc.identifier.eissn | 2314-6141 | - |
dc.identifier.artn | 598129 | - |
dc.description.validate | 201812 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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You_Large-Scale_Protein-Protein_Interactions.pdf | 1.43 MB | Adobe PDF | View/Open |
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