Advances in the prediction of protein targeting signals

  • Schneider G
  • Fechner U
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Abstract

Enlarged sets of reference data and special machine learning approaches have improved the accuracy of the prediction of protein subcellular localization. Recent approaches report over 95% correct predictions with low fractions of false-positives for secretory proteins. A clear trend is to develop specifically tailored organism- and organelle-specific prediction tools rather than using one general method. Focus of the review is on machine learning systems, highlighting four concepts: the artificial neural feed-forward network, the self-organizing map (SOM), the Hidden-Markov-Model (HMM), and the support vector machine (SVM).

Author-supplied keywords

  • Bioinformatics
  • Machine learning
  • Review
  • Signal peptidase
  • Signal sequence

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