Format:
Online-Ressource
ISSN:
1615-9861
Content:
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).
In:
volume:4
In:
number:6
In:
year:2004
In:
pages:1571-1580
In:
extent:10
In:
Proteomics, Weinheim : Wiley VCH, [2001]-, 4, Heft 6 (2004), 1571-1580 (gesamt 10), 1615-9861
Language:
English
DOI:
10.1002/pmic.200300786
URN:
urn:nbn:de:101:1-2023101607505923460965
URL:
https://doi.org/10.1002/pmic.200300786
URL:
https://nbn-resolving.org/urn:nbn:de:101:1-2023101607505923460965
URL:
https://d-nb.info/1306197694/34
URL:
https://doi.org/10.1002/pmic.200300786
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