Semi-supervised prediction of protein interaction sentences exploiting semantically encoded metrics

Protein-protein interaction (PPI) identification is an integral component of many biomedical research and database curation tools. Automation of this task through classification is one of the key goals of text mining (TM). However, labelled PPI corpora required to train classifiers are generally small. In order to overcome this sparsity in the training data, we propose a novel method of integrating corpora that do not contain relevance judgements. Our approach uses a semantic language model to gather word similarity from a large unlabelled corpus. This additional information is integrated into the sentence classification process using kernel transformations and has a re-weighting effect on the training features that leads to an 8% improvement in F-score over the baseline results. Furthermore, we discover that some words which are generally considered indicative of interactions are actually neutralised by this process

Protein interaction sentence detection using multiple semantic kernels

<p>Abstract</p> <p>Background</p> <p>Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection.</p> <p>Results</p> <p>We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels.</p> <p>Conclusions</p> <p>The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins.</p