title: Advances in the prediction of protein targeting signals.
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).
authors.affiliation: Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Frankfurt, Germany. gisbert.schneider@modlab.de
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
authors.forename: Uli
authors.initials: U
authors.lastname: Fechner
mesh_terms: 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
chemical_list: D021382:Protein Sorting Signals; D011506:Proteins