Biological Literature Miner:
Gene Mention Recognition and Protein-Protein Interaction Pair Extraction
CHEN Yifei
ABSTRACT
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In this dissertation we describe a machine learning-based text
mining system to extract automatically useful information from the
biological literature: Biological Literature Miner
(BioLMiner). BioLMiner is designed, implemented and tuned in
collaboration with Feng Liu and includes four
subsystems: a Gene Mention Recognizer (GMRer), a Gene
Normalizer (GNer), an Interaction Article Classifier
(IACer) and a Protein-Protein Interaction Pair Extractor
(PPIEor). The two subsystems GMRer and PPIEor
are the topic of this dissertation and they are based on
Support Vector Machines (SVMs) and Conditional Random
Fields (CRFs).
GMRer is the first subsystem in BioLMiner and
solves the task of recognizing gene mentions, i.e. gene and protein
names, in the biological literature. This is a very important step
for the next subsystems that are using these
extracted gene mentions in order to extract further information. In
GMRer both a sequence labeling-based model using CRFs and two
classification-based models using forward SVMs and backward SVMs
are used, respectively. To improve the performance further,
a hybrid model is proposed that fuses, using voting rules, the outputs
of the three
individual models.
PPIEor is the last subsystem of BioLMiner and its
purpose is to automatically extract protein-protein interaction
(PPI) pairs from the biological literature. Finding these
interactions is one of the most pressing problems in the biological
domain. During the preprocessing phase, PPIEor transforms the
sentence-based articles into clause-based ones and the candidate PPI
pairs are distilled. A binary SVM classifier is proposed to predict
whether the candidate PPI pairs are correct or not. Finally,
a post-processor recovers some self-interaction proteins
which cannot be identified by the SVM model.