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

Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts

BACKGROUND: The Variome corpus, a small collection of published articles about inherited colorectal cancer, includes annotations of 11 entity types and 13 relation types related to the curation of the relationship between genetic variation and disease. Due to the richness of these annotations, the corpus provides a good testbed for evaluation of biomedical literature information extraction systems. METHODS: In this paper, we focus on assessing performance on extracting the relations in the corpus, using gold standard entities as a starting point, to establish a baseline for extraction of relations important for extraction of genetic variant information from the literature. We test the application of the Public Knowledge Discovery Engine for Java (PKDE4J) system, a natural language processing system designed for information extraction of entities and relations in text, on the relation extraction task using this corpus. RESULTS: For the relations which are attested at least 100 times in the Variome corpus, we realise a performance ranging from 0.78-0.84 Precision-weighted F-score, depending on the relation. We find that the PKDE4J system adapted straightforwardly to the range of relation types represented in the corpus; some extensions to the original methodology were required to adapt to the multi-relational classification context. The results are competitive with state-of-the-art relation extraction performance on more heavily studied corpora, although the analysis shows that the Recall of a co-occurrence baseline outweighs the benefit of improved Precision for many relations, indicating the value of simple semantic constraints on relations. CONCLUSIONS: This work represents the first attempt to apply relation extraction methods to the Variome corpus. The results demonstrate that automated methods have good potential to structure the information expressed in the published literature related to genetic variants, connecting mutations to genes, diseases, and patient cohorts. Further development of such approaches will facilitate more efficient biocuration of genetic variant information into structured databases, leveraging the knowledge embedded in the vast publication literature