In:
PROTEOMICS, Wiley, Vol. 4, No. 6 ( 2004-06), p. 1571-1580
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).
Type of Medium:
Online Resource
ISSN:
1615-9853
,
1615-9861
DOI:
10.1002/pmic.200300786
Language:
English
Publisher:
Wiley
Publication Date:
2004
detail.hit.zdb_id:
2037674-1
SSG:
12
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