Optimizing syntax patterns for discovering protein-protein interactions

Abstract

We propose a method for automated extraction of proteinprotein interactions from scientific text. Our system matches sentences against syntax patterns typically describing protein interactions. We define a set of 22 patterns, each a regular expression consisting of anchor positions and parameterizable constraints. This small set is then refined and optimized using a genetic algorithm on a training set. No heuristic definitions are necessary, and the final pattern set can be generated completely without manual curation. Our method can be applied to any syntax pattern-based protein-protein interaction system and thus complements related work on building comprehensive sets of such patterns. The application of different fitness-functions during evolution provides an easy way to tune the system either toward precision, recall, or f-measure. We evaluate our system on two samples, one derived from the BioCreAtIvE corpus, the other from references in the Dip. The automatic refinement of patterns adds up to 16% to the precision, and 5% to the recall of our system. We additionally study the impact of a proper protein name recognition, which could improve precision by about 17% and recall by 12%.

Extract Protein-Protein Interactions from the Literature Using Support Vector Machines with Feature Selection

“…We construct a lexicon called iLexicon that consists of interaction nouns and verbs similar to the ones proposed by Plake et al (2005). Then we refine and extend iLexicon based on the training data of Data FEBS .…”

“…These gaps are limited in length but they do not require particular words. As recommend in Plake et al (2005) we have set the maximum length of the gaps equal to 5. …”

Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

“…We also performed the classification task on unseen data set and compared the results to the output from conventional ML algorithms. Table 1 reports precision rates at top N th rank sentences for Biocreative I corpus (BC) [13], Christine Brun corpus (CB) [10] and Negative Cases of ProteinProtein interaction corpus (N-PPI) [14], respectively. The hypernetwork classifier outputs acceptable results compared to SVM with tree kernel, naive Bayes, and SVM with RBF kernels.…”

Protein–protein interaction identification using a hybrid model

“…Co-occurrence analysis is the most straightforward approach and generally results in high recall but low precision [9,10]. Some other approaches construct patterns specifying how an interaction is described in literature and use them as rules to find PPIs [11][12][13][14][15][16]. Rule or pattern-based approaches can increase precision but significantly lower recall.…”