Advances in the prediction of protein targeting signals |
| |
Authors: | Schneider Gisbert Fechner Uli |
| |
Affiliation: | Johann Wolfgang Goethe-Universit?t, Institut für Organische Chemie und Chemische Biologie, Frankfurt, Germany. gisbert.schneider@modlab.de |
| |
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). |
| |
Keywords: | |
本文献已被 PubMed 等数据库收录! |
|