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A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains
August 2005 (vol. 17 no. 8)
pp. 1051-1064
ASCII Text
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Nobuhisa Ueda, Kiyoko F. Aoki-Kinoshita, Atsuko Yamaguchi, Tatsuya Akutsu, Hiroshi Mamitsuka, "A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 8, pp. 1051-1064, August, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2005.117,
author = {Nobuhisa Ueda and Kiyoko F. Aoki-Kinoshita and Atsuko Yamaguchi and Tatsuya Akutsu and Hiroshi Mamitsuka},
title = {A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {8},
issn = {1041-4347},
year = {2005},
pages = {1051-1064},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.117},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains
IS - 8
SN - 1041-4347
SP1051
EP1064
EPD - 1051-1064
A1 - Nobuhisa Ueda,
A1 - Kiyoko F. Aoki-Kinoshita,
A1 - Atsuko Yamaguchi,
A1 - Tatsuya Akutsu,
A1 - Hiroshi Mamitsuka,
PY - 2005
KW - Index Terms- Biology and genetics
KW - machine learning
KW - data mining
KW - mining methods and algorithms.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Glycans, or carbohydrate sugar chains, which play a number of important roles in the development and functioning of multicellular organisms, can be regarded as labeled ordered trees. A recent increase in the documentation of glycan structures, especially in the form of database curation, has made mining glycans important for the understanding of living cells. We propose a probabilistic model for mining labeled ordered trees, and we further present an efficient learning algorithm for this model, based on an EM algorithm. The time and space complexities of this algorithm are rather favorable, falling within the practical limits set by a variety of existing probabilistic models, including stochastic context-free grammars. Experimental results have shown that, in a supervised problem setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. We further applied the proposed method to aligning multiple glycan trees, and we detected biologically significant common subtrees in these alignments where the trees are automatically classified into subtypes already known in glycobiology. Extended abstracts of parts of the work presented in this paper have appeared in [35], [4], and [3].

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Index Terms:
Index Terms- Biology and genetics, machine learning, data mining, mining methods and algorithms.
Citation:
Nobuhisa Ueda, Kiyoko F. Aoki-Kinoshita, Atsuko Yamaguchi, Tatsuya Akutsu, Hiroshi Mamitsuka, "A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 8, pp. 1051-1064, Aug. 2005, doi:10.1109/TKDE.2005.117
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