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

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dc.creatorAbi-Haidar, Alaa
dc.creatorKaur, Jasleen
dc.creatorMaguitman, Ana G.
dc.creatorRadivojac, Predrag
dc.creatorRetchsteiner, Andreas
dc.creatorVerspoor, Karin
dc.creatorWang, Zhiping
dc.creatorRocha, Luis M.
dc.date2008-12-04
dc.date.accessioned2026-07-07T12:09:45Z
dc.date.available2026-07-07T12:09:45Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/0812.1029
dc.identifierhttp://arxiv.org/abs/0812.1029
dc.identifierGenome Biology 2008, 9(Suppl 2):S11
dc.identifierdoi:10.1186/gb-2008-9-s2-s11
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/209731
dc.subjectInformation Retrieval
dc.subjectMachine Learning
dc.titleUncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
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