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2015 ; 2015
(ä): 254838
Nephropedia Template TP
gab.com Text
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English Wikipedia
METSP: a maximum-entropy classifier based text mining tool for
transporter-substrate identification with semistructured text
#MMPMID26495291
Zhao M
; Chen Y
; Qu D
; Qu H
Biomed Res Int
2015[]; 2015
(ä): 254838
PMID26495291
show ga
The substrates of a transporter are not only useful for inferring function of the
transporter, but also important to discover compound-compound interaction and to
reconstruct metabolic pathway. Though plenty of data has been accumulated with
the developing of new technologies such as in vitro transporter assays, the
search for substrates of transporters is far from complete. In this article, we
introduce METSP, a maximum-entropy classifier devoted to retrieve
transporter-substrate pairs (TSPs) from semistructured text. Based on the high
quality annotation from UniProt, METSP achieves high precision and recall in
cross-validation experiments. When METSP is applied to 182,829 human transporter
annotation sentences in UniProt, it identifies 3942 sentences with transporter
and compound information. Finally, 1547 confidential human TSPs are identified
for further manual curation, among which 58.37% pairs with novel substrates not
annotated in public transporter databases. METSP is the first efficient tool to
extract TSPs from semistructured annotation text in UniProt. This tool can help
to determine the precise substrates and drugs of transporters, thus facilitating
drug-target prediction, metabolic network reconstruction, and literature
classification.
|*Natural Language Processing
[MESH]
|*Vocabulary, Controlled
[MESH]
|Algorithms
[MESH]
|Data Mining/*methods
[MESH]
|Entropy
[MESH]
|Machine Learning
[MESH]
|Membrane Transport Proteins/*chemistry/*metabolism
[MESH]
free
free
free
