Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics

Polajnar, Tamara; Girolami, Mark

doi:10.1007/978-3-642-04031-3_24

Cited by 1 publication

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“…Thus, to study the novelty of this approach we eliminate, as much as possible, reliance on further language processing algorithms. Consequently, by examining a simpler task, we produce results that are not directly comparable with the kernel-based PPI extraction methods described in [ 26 ], but are comparable to the baseline results described in [ 34 , 37 ]. However, the approach described here is modular, and can be augmented for use with methods that rely on deep linguistic features.…”

Section: Introductionmentioning

confidence: 88%

Protein interaction sentence detection using multiple semantic kernels

2011

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BackgroundDetection 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.ResultsWe 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.ConclusionsThe 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.

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Section: Introductionmentioning

confidence: 88%