Literature mining of protein-residue associations with graph rules learned through distant supervision

<p>Abstract</p> <p>Background</p> <p>We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model.</p> <p>Results</p> <p>The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods.</p> <p>Conclusions</p> <p>The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.</p

Enhancing automatic extration of biomedical relations using different linguistic features extracted from text

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 08-06-2017La extracción de relaciones entre entidades es una tarea muy importante dentro del procesamiento de textos biomédicos. Cada vez hay más información sobre este tipo de interacciones almacenada en bases de datos, pero sin embargo la mayor cantidad de información relacionada con el tema está presente en artículos científicos o en recursos donde la información se almacena en formato textual.Las interacciones entre fármacos son, en particular, una preocupación generalizada en medicina, por esa razón la extracción automática de este tipo de relaciones es una tarea muy demandada en el procesamiento de textos biomédicos. Una interacción entre 2 fármacos normalmente se produce cuando un fármaco altera el nivel de actividad de otro fármaco. De acuerdo a los informes presentados por la Adminsitración Nacional de Alimentos y Fármacos de Estados Unidos y otros estudios reconocidos [1], cada año se producen más de 2 millones de interacciones mortales entre fármacos. Muchos investigadores y compañías farmaceúticas han desarrollado bases de datos donde estas interacciones son almacenadas. Sin embargo, la información más actualizada y valiosa sigue apareciendo sólo en documentos no estructurados en formato textual, incluyendo publicaciones científicas e informes técnicos.En esta tesis se estudian 3 conjuntos de características lingüísticas de los textos: negación,dependencia clausal y candidatos neutros. El objetivo final de la investigación es mejorar el rendimiento de la tarea de extracción de interacciones entre fármacos considerando las combinaciones de las características lingüísticas extraídas de los textos con métodos de aprendizaje basados en kernel...Extracting biomedical relations from texts is a relatively new, but rapidly growing researchfield in natural language processing (NLP). Due to the increasing number of biomedicalresearch publications and the key role of databases of biomedical relations in biological andmedical research, extracting biomedical relations from scientific articles and text resourcesis of utmost importance.Drug-drug interactions (DDI) are, in particular, a widespread concern in medicine, and thus,extracting this kind of interactions automatically from texts is of high demand in BioNLP. Adrug-drug interaction usually occurs when one drug alters the activity level of another drug.According to the reports prepared by the U. S. Food and Drug Administration (the FDA) andother acknowledged studies [1], over 2 million life-threatening DDIs occur in the UnitedStates every year. Many academic researchers and pharmaceutical companies havedeveloped relational and structural databases, where DDIs are recorded. Nevertheless,most up-to-date and valuable information is still found only in unstructured research textdocuments, including scientific publications and technical reports.In this thesis, three complementary, linguistically driven, feature sets, are studied: negation,clause dependency, and neutral candidates. The ultimate aim of this research is to enhancethe performance of the DDI extraction task by considering the combinations of theextracted features with well-established kernel methods...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Procedurally Rhetorical Verb-Centric Frame Semantics as a Knowledge Representation for Argumentation Analysis of Biochemistry Articles

The central focus of this thesis is rhetorical moves in biochemistry articles. Kanoksilapatham has provided a descriptive theory of rhetorical moves that extends Swales' CARS model to the complete biochemistry article. The thesis begins the construction of a computational model of this descriptive theory. Attention is placed on the Methods section of the articles. We hypothesize that because authors' argumentation closely follows their experimental procedure, procedural verbs may be the guide to understanding the rhetorical moves. Our work proposes an extension to the normal (i.e., VerbNet) semantic roles especially tuned to this domain. A major contribution is a corpus of Method sections that have been marked up for rhetorical moves and semantic roles. The writing style of this genre tends to occasionally omit semantic roles, so another important contribution is a prototype ontology that provides experimental procedure knowledge for the biochemistry domain. Our computational model employs machine learning to build its models for the semantic roles and rhetorical moves, validated against a gold standard reflecting the annotation of these texts by human experts. We provide significant insights into how to derive these annotations, and as such have contributions as well to the general challenge of producing markups in the domain of biomedical science documents, where specialized knowledge is required