Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks

Testing extensive use of NER tools in article classification and a statistical approach for method interaction extraction in the protein-protein interaction literature

We participated (as Team 81) in the Article Classification (ACT) and Interaction Method (IMT) subtasks of the Protein-Protein Interaction task of the Biocreative III Challenge. For the ACT we pursued an extensive testing of available Named Entity Recognition (NER) tools, and used the most promising ones to extend our the Variable Trigonometric Threshold (VTT) linear classifier we successfully used in BioCreative II and II.5. Our main goal was to exploit the power of available NER tools to aid in the document classification of documents relevant for Protein-Protein Interaction. We also used a Support Vector Machine Classifier on NER features for comparison purposes. For the IMT, we experimented with a primarily statistical approach, as opposed to a deeper natural language processing strategy; in a nutshell, we exploited classifiers, simple pattern matching, and ranking of candidate matches using statistical considerations. We will also report on our efforts to integrate our IMT method sentence classifier into our ACT pipeline

Unsupervised Named Entity Recognition and Disambiguation: An Application to Old French Journals

International audienceIn this paper we introduce our method of Unsupervised Named Entity Recognition and Disambiguation (UNERD) that we test on a recently digitized unlabeled corpus of French journals comprising 260 issues from the 19th century. Our study focuses on detecting person, location, and organization names in text. Our original method uses a French entity knowledge base along with a statistical contextual disambiguation approach. We show that our method outperforms supervised approaches when trained on small amounts of annotated data, since manual data annotation is very expensive and time consuming, especially in foreign languages and specific domains