Advances in the prediction of protein targeting signals

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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).