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1 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

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Author(s):

Hsiao, HCW (Hsiao, Han C. W.); Chen, SH (Chen, Shih
-
Hao); Chang, JPC
(Chang, Judson Pei
-
Chun); Tsai, JJP (Tsai, Jeffrey J. P.)

Title:

Predicting subcellular locations of eukaryotic proteins using Bayesian and k
-
nearest
neighbor classifiers

Sou
rce:

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 24 (5): 1361
-
1375
SEP 2008

Language:

English

Document Type:

Article

Author Keywords:

subcellular location prediction; naive Bayesian classifier; k
-
nearest
neighbor classifier; functional domain; featu
re reduction

KeyWords Plus:

SUPPORT VECTOR MACHINES; FUNCTIONAL DOMAIN COMPOSITION;
AMINO
-
ACID
-
COMPOSITION; GRAM
-
NEGATIVE BACTERIA; LOCALIZATION
PREDICTION; SEQUENCE; GENOME; SITES; YEAST

Abstract:

Biologically, the function of a protein is highly relate
d to its subcellular location. It is
of necessity to develop a reliable method for protein subcellular location prediction, especially
when a large amount of proteins are to be analyzed. Various methods have been proposed to
perform the task. The results,
however, are not satisfactory in terms of effectiveness and
efficiency. A hybrid approach combining naive Bayesian classifier and k
-
nearest neighbor
classifier is proposed to classify eukaryotic proteins represented as a combination of amino
acid compositi
on, dipeptide composition, and functional domain composition. Experimental
results show that the total accuracy of a set of 17,655 proteins can reach up to 91.5%.

Addresses:

[Hsiao, Han C. W.; Chen, Shih
-
Hao; Chang, Judson Pei
-
Chun] Asia Univ, Dept
Bioinf
ormat, Wufeng 413, Taiwan; [Tsai, Jeffrey J. P.] Univ Illinois, Dept Comp Sci, Chicago, IL
60607 USA

Reprint Address:

Hsiao, HCW, Asia Univ, Dept Bioinformat, Wufeng 413, Taiwan.

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Cited Reference Count:

42

Times Cited:

0

Publisher:

INST INFORMATION SCIENCE

Publisher Address:

ACADEMIA SINICA, TAIPEI 115, TAIWAN

ISSN:

1016
-
2364

29
-
char Source Abbrev.:

J INF SCI ENG

ISO Source Abbrev.:

J. In
f. Sci. Eng.

Source Item Page Count:

15

Subject Category:

Computer Science, Information Systems

ISI Document Delivery No.:

354TU