University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Medical Biology
  • Medical Biology - Research Publications
  • View Item
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Medical Biology
  • Medical Biology - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators.

    Thumbnail
    Download
    Published version (2.528Mb)

    Citations
    Scopus
    Altmetric
    10
    Author
    Muthukrishnan, S; Puri, M; Lefevre, C
    Date
    2014-01-27
    Source Title
    BMC Research Notes
    Publisher
    Springer Science and Business Media LLC
    University of Melbourne Author/s
    Lefevre, Christophe
    Affiliation
    Medical Biology (W.E.H.I.)
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Muthukrishnan, S., Puri, M. & Lefevre, C. (2014). Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators.. BMC Res Notes, 7 (1), pp.63-. https://doi.org/10.1186/1756-0500-7-63.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253437
    DOI
    10.1186/1756-0500-7-63
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3924408
    Abstract
    BACKGROUND: Plasminogen (Pg), the precursor of the proteolytic and fibrinolytic enzyme of blood, is converted to the active enzyme plasmin (Pm) by different plasminogen activators (tissue plasminogen activators and urokinase), including the bacterial activators streptokinase and staphylokinase, which activate Pg to Pm and thus are used clinically for thrombolysis. The identification of Pg-activators is therefore an important step in understanding their functional mechanism and derives new therapies. METHODS: In this study, different computational methods for predicting plasminogen activator peptide sequences with high accuracy were investigated, including support vector machines (SVM) based on amino acid (AC), dipeptide composition (DC), PSSM profile and Hybrid methods used to predict different Pg-activators from both prokaryotic and eukaryotic origins. RESULTS: Overall maximum accuracy, evaluated using the five-fold cross validation technique, was 88.37%, 84.32%, 87.61%, 85.63% in 0.87, 0.83,0.86 and 0.85 MCC with amino (AC) or dipeptide composition (DC), PSSM profile and Hybrid methods respectively. Through this study, we have found that the different subfamilies of Pg-activators are quite closely correlated in terms of amino, dipeptide, PSSM and Hybrid compositions. Therefore, our prediction results show that plasminogen activators are predictable with a high accuracy from their primary sequence. Prediction performance was also cross-checked by confusion matrix and ROC (Receiver operating characteristics) analysis. A web server to facilitate the prediction of Pg-activators from primary sequence data was implemented. CONCLUSION: The results show that dipeptide, PSSM profile, and Hybrid based methods perform better than single amino acid composition (AC). Furthermore, we also have developed a web server, which predicts the Pg-activators and their classification (available online at http://mamsap.it.deakin.edu.au/plas_pred/home.html). Our experimental results show that our approaches are faster and achieve generally a good prediction performance.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [45689]
    • Medical Biology - Research Publications [865]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors