Advances in the prediction of protein targeting signals.
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).
Schneider
Gisbert
G
Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Frankfurt, Germany. gisbert.schneider@modlab.de
Fechner
Uli
U
D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D005189:False Positive Reactions; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals; D011506:Proteins / Q000378:metabolism
D021382:Protein Sorting Signals; D011506:Proteins