Automatic functional annotation of predicted active sites - European ...

For example, ”hunchback” is a protein in Drosophila, while it is also a general English
term. Furthermore, protein names consist mostly of multiple words, e.g. ”Rho-like protein”
or ”HIV-1 envelope glycoprotein gp120”. An ER system needs to identify all the
constituents of a protein name in order to relate the detected entity to its reference entry
in a database. The BioCreAtIvE challenge addressed this problem with the 1B subtask;
the target is the identification of protein/gene names in text, and the annotation of their
correct gene identifier. Various solutions were published ranging from rule-based methods
[HFM + 05] [TW02] [Fuk98] to machine learning approaches [CMP05]. The developed
methods are, in general, reusable for any other biological entity recognition or terminology
identification problem.
Works have also been published that focused on the extraction of protein point mutations
[RSMA + 04] [HLC04] [BW05] [LHC07] [YLPV07], which is one category of protein
residue terminology. Other categories are residue sequence or residue interaction pair.
The most widely adopted method to identify these terminologies is the design of regular
expression patterns.
2.3.2 Biological relation extraction
Relation extraction (RD) aims to find associations between entities, or between an entity
and a terminology within a text phrase. One objective in biomedical information
extraction is the mining of biological facts from text. An example of biological fact is
the semantic relation between two biological entities, such as protein-protein interaction
[TOT04].
Until now, three strategies have been investigated for biological relation extraction: the
co-occurrence based analysis [LC05] [SB05], pattern-based approach [HZH + 04] [LCM03],
and machine learning based methods [BM05] [BM06]. The common limitation of all of
these extraction systems is, that only the relation targets, e.g. proteins within a proteinprotein
interaction, are extracted. By no means are contextual information considered in
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