Antigenic: An improved prediction model of protective antigens.
An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance i...
| Publicado en: | Artificial Intelligence in Medicine Vol. 94; pp. 28 - 42 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Mar2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135227270&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135227270 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Mar2019 vid: 94 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135227270 135227270 NLM30871681 10.1016/j.artmed.2018.12.010 NLM30871681 135227270 ppf: 28 ppct: 14 formats: tig: atl: Antigenic: An improved prediction model of protective antigens. aug: au: Rahman, M. Saifur Rahman, Md. Khaledur Saha, Sanjay Kaykobad, M. Rahman, M. Sohel affil: Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205, Bangladesh sug: subj: Antigens Analysis Models, Biological Algorithms Antigens Bioinformatics Methods Amino Acids Analysis Clinical Assessment Tools ab: An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. However, the laboratory experiments to isolate and identify antigens from a microbial pathogen are expensive, time consuming and often unsuccessful. This is why Reverse Vaccinology has become the modern trend of vaccine search, where computational methods are first applied to predict protective antigens or their determinants, known as epitopes. In this paper, we propose a novel, accurate computational model to identify protective antigens efficiently. Our model extracts features directly from the protein sequences, without any dependence on functional domain or structural information. After relevant features are extracted, we have used Random Forest algorithm to rank the features. Then Recursive Feature Elimination (RFE) and minimum redundancy maximum relevance (mRMR) criterion were applied to extract an optimal set of features. The learning model was trained using Random Forest algorithm. Named as Antigenic, our proposed model demonstrates superior performance compared to the state-of-the-art predictors on a benchmark dataset. Antigenic achieves accuracy, sensitivity and specificity values of 78.04%, 78.99% and 77.08% in 10-fold cross-validation testing respectively. In jackknife cross-validation, the corresponding scores are 80.03%, 80.90% and 79.16% respectively. The source code of Antigenic, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/AntigenPredictor. A publicly accessible web interface has also been established at: http://antigenic.research.buet.ac.bd. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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