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  • 1.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    You, Liwen
    Lund University, Department of Theoretical Physics, Lund, Sweden.
    Why neural networks should not be used for HIV-1 protease cleavage site prediction2004Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, nr 11, s. 1702-1709Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Several papers have been published where non-linear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. Motivation: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. Results: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers.

  • 2.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    You, Liwen
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Garwicz, Daniel
    Uppsala University, Uppsala, Sweden.
    State of the art prediction of HIV-1 protease cleavage sites2015Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, nr 8, s. 1204-1210Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motivation: Understanding the substrate specificity of HIV-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

    Results: The linear support vector machine with orthogonal encod-ing is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor ser-vices. It is also found that schemes using physicochemical proper-ties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.

    Availability: The data sets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available. © 2014 The Author.

  • 3.
    Samuelsson, Jim
    et al.
    Genedata GmbH, Lena-Christ-Strasse 50, 82152 Martinsried, Germany.
    Dalevi, Daniel
    Computing Science, Chalmers University of Technology, SE-412 96 Göteborg.
    Levander, Fredrik
    Department of Protein Technology, Lund University, Sölvegatan 33A.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting2004Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 20, nr 18, s. 3628-3635Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified.

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