Semantically linking molecular entities in literature through entity relationships

Background Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts. Results We describe, compare and evaluate two frameworks developed for the prediction of non-causal or 'entity' relations (REL) between gene symbols and domain terms. For the corresponding REL challenge of the BioNLP Shared Task of 2011, these systems ranked first (57.7% F-score) and second (41.6% F-score). In this paper, we investigate the performance discrepancy of 16 percentage points by benchmarking on a related and more extensive dataset, analysing the contribution of both the term detection and relation extraction modules. We further construct a hybrid system combining the two frameworks and experiment with intersection and union combinations, achieving respectively high-precision and high-recall results. Finally, we highlight extremely high-performance results (F-score > 90%) obtained for the specific subclass of embedded entity relations that are essential for integrating text mining predictions with database facts. Conclusions The results from this study will enable us in the near future to annotate semantic relations between molecular entities in the entire scientific literature available through PubMed. The recent release of the EVEX dataset, containing biomolecular event predictions for millions of PubMed articles, is an interesting and exciting opportunity to overlay these entity relations with event predictions on a literature-wide scale

Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations

Technological advancements in the field of genetics have led not only to an abundance of experimental data, but also caused an exponential increase of the number of published biomolecular studies. Text mining is widely accepted as a promising technique to help researchers in the life sciences deal with the amount of available literature. This paper presents a freely available web application built on top of 21.3 million detailed biomolecular events extracted from all PubMed abstracts. These text mining results were generated by a state-of-the-art event extraction system and enriched with gene family associations and abstract generalizations, accounting for lexical variants and synonymy. The EVEX resource locates relevant literature on phosphorylation, regulation targets, binding partners, and several other biomolecular events and assigns confidence values to these events. The search function accepts official gene/protein symbols as well as common names from all species. Finally, the web application is a powerful tool for generating homology-based hypotheses as well as novel, indirect associations between genes and proteins such as coregulators

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.Siirretty Doriast

Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011

We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties

The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation

Philosophiae Doctor - PhDNeisseria meningitidis (the meningococcus), the causative agent of meningococcal disease (MD) was identified in 1887 and despite effective antibiotics and partially effective vaccines, Neisseria meningitidis (N. meningitidis) is the leading cause worldwide of meningitis and rapidly fatal sepsis usually in otherwise healthy individuals. Over 500 000 meningococcal cases occur every year. These numbers have made bacterial meningitis a top ten infectious cause of death worldwide. MD primarily affects children under 5 years of age, although in epidemic outbreaks there is a shift in disease to older children, adolescents and adults. MD is also associated with marked morbidity including limb loss, hearing loss, cognitive dysfunction, visual impairment, educational difficulties, developmental delays, motor nerve deficits, seizure disorders and behavioural problems. Antimicrobial peptides (AMPs) are molecules that provide protection against environmental pathogens, acting against a large number of microorganisms, including bacteria, fungi, yeast and virus. AMPs production is a major component of innate immunity against infection. The chemical properties of AMPs allow them to insert into the anionic cell wall and phospholipid membranes of microorganisms or bind to the bacteria making it easily detectable for diagnostic purposes. AMPs can be exploited for the generation of novel antibiotics, as biomarkers in the diagnosis of inflammatory conditions, for the manipulation of the inflammatory process, wound healing, autoimmunity and in the combat of tumour cells. Due to the severity of meningitis, early detection and identification of the strain of N. meningitidis is vital. Rapid and accurate diagnosis is essential for optimal management of patients and a major problem for MD is its diagnostic difficulties and experts conclude that with an early intervention the patient’ prognosis will be much improved. It is becoming increasingly difficult to confirm the diagnosis of meningococcal infection by conventional methods. Although polymerase chain reaction (PCR) has the potential advantage of providing more rapid confirmation of the presence of the bacterium than culturing, it is still time consuming as well as costly. Introduction of AMPs to bind to N. meningitidis receptors could provide a less costly and time consuming solution to the current diagnostic problems. World Health Organization (WHO) meningococcal meningitis program activities encourage laboratory strengthening to ensure prompt and accurate diagnosis to rapidly confirm the presence of MD. This study aimed to identify a list of putative AMPs showing antibacterial activity to N. meningitidis to be used as ligands against receptors uniquely expressed by the bacterium and for the identified AMPs to be used in a Lateral Flow Device (LFD) for the rapid and accurate diagnosis of MD

Text Mining and Medicine: An approach to early detection of diseases

El futuro próximo de los servicios sanitarios vendrá marcado por el envejecimiento de la población y la cronicidad de las enfermedades. Junto a los cambios demográficos y sociales, se está produciendo un claro aumento de la frecuentación en los distintos servicios de atención primaria y especializada y, por supuesto, todo esto se traduce en un fuerte incremento del gasto sanitario. Todo este problemático contexto hace que las instituciones sanitarias se marquen como principales objetivos la priorización de la prevención, el control de los factores de riesgo y la detección precoz de enfermedades. Para apoyar la prevención primaria es muy importante que el profesional sanitario tenga todos los medios disponibles a su alcance para extraer conocimiento de su principal fuente de información que es la historia clínica informatizada del paciente. Así, el profesional sanitario debería disponer de herramientas que permitan conocer e interrelacionar eventos clínicos de interés, alertar sobre la aparición de futuros riesgos para la salud o pronosticar el posible desarrollo de una enfermedad. Sin embargo, el esfuerzo, tiempo y coste que supondría extraer este conocimiento de la simple lectura de los múltiples informes clínicos contenidos en la historia de un paciente (escritos en su mayoría en lenguaje natural), sería incalculable e imposible de asumir por la mayoría de los profesionales sanitarios en la clínica diaria. Hasta el momento, los sistemas de información existentes en la mayoría de instituciones sanitarias sólo han sido utilizados como sistemas de almacenaje de información, es decir sistemas que recopilan y almacenan toda la información asistencial generada en la interacción médico-paciente, pero todavía no se ha dado el paso de convertir estos grandes “almacenes de información” en “fuentes de conocimiento” que aporten valor para facilitar y apoyar la toma de decisiones clínicas. Sin embargo, el reto de automatizar este proceso, transformar almacenes de información en fuentes de conocimiento, no es una tarea trivial. Se estima que en un complejo hospitalario regional se pueden generar al año más de 3 millones de documentos clínicos, el 80% de esta documentación clínica contiene información no estructurada, una de la más destacable es la información textual. Hasta ahora la información clínica textual ha sido prácticamente ignorada por la mayoría de las instituciones sanitarias debido a la gran complejidad en su explotación para generar valor de su contenido. La principal fuente de conocimiento contenida en la historia clínica electrónica, que es la narrativa clínica textual, es en la práctica altamente desaprovechada. A la dificultad de las organizaciones sanitarias para obtener valor del texto, con las herramientas de análisis hasta ahora utilizadas, se suman las peculiares características que posee la terminología clínica donde prima: una alta ambigüedad y complejidad del vocabulario, la narrativa textual libre, una escasa normalización terminológica y un uso excesivo de acrónimos y negaciones. En este complejo marco y ante la creciente necesidad de adquirir conocimiento para apoyar el proceso de prevención y toma de decisiones clínicas, se hace imprescindible el uso de Sistemas Inteligentes que ayuden a extraer el valor encerrado en el contenido textual de los múltiples documentos que integran la historia clínica electrónica. Pero a pesar de esta acuciante necesidad, actualmente existen muy pocos sistemas reales que extraigan conocimiento del texto clínico para facilitar el trabajo diario al profesional sanitario en tareas arduas y complejas como la detección de factores de riesgo o la predicción diagnóstica. En la actualidad, para abordar la problemática de extraer valor del texto clínico, en el entorno de la medicina computacional, disponemos de las técnicas avanzadas que nos proporciona la disciplina de la Minería de Textos (MT). Esta disciplina puede definirse como un área orientada a la identificación y extracción de nuevo conocimiento adquirido a partir de información textual, es un campo multidisciplinar que puede integrar técnicas de otras disciplinas como el Procesamiento del Lenguaje Natural (PLN) o Aprendizaje Automático (AA). En este sentido, abordamos esta tesis doctoral con un análisis exhaustivo y pormenorizado del estado del arte sobre la disciplina de la MT en el ámbito de la Medicina, recogiendo los métodos, técnicas, tareas, recursos y tendencias más destacadas en la literatura. De esta amplia revisión se detecta que en la práctica los sistemas existentes para apoyar el proceso de toma de decisiones clínicas basados en información clínica textual son escasos y generalmente resuelven una única tarea principal centrándose en un área específica de conocimiento y siendo desarrollados para dominios muy específicos difícilmente reproducibles en otros entornos. Ante las problemáticas observadas en los sistemas de MT existentes y las necesidades de las instituciones sanitarias, se propone la creación de un novedoso sistema, denominado MiNerDoc, que permita apoyar la toma de decisiones clínicas en base a una combinación de técnicas de la disciplina de la MT, junto con el enriquecimiento terminológico y semántico proporcionado por la herramienta MetaMap y el metathesaurus UMLS, recursos que aportan características esenciales en el dominio médico. MiNerDoc permite, entre otras funcionalidades, detectar factores de riesgo o eventos clínicos de interés e inferir automáticamente códigos normalizados de diagnósticos tomando como fuente exclusiva la información textual contenida en informes clínicos, en definitiva, permite llevar a cabo tareas complejas que facilitan y apoyan la labor del profesional sanitario en la prevención primaria y la toma de decisiones clínicas. El sistema de MT propuesto ha sido evaluado en base a un amplio análisis experimental, los resultados demostraron la efectividad y viabilidad del sistema propuesto y verificaron el prometedor rendimiento de MiNerDoc en las dos tareas evaluadas, reconocimiento de entidades médicas y clasificación diagnóstica multietiqueta.The near future of health services will be marked by the ageing of the population and the chronicity of diseases. Together with the demographic and social changes, there is a clear increase in the number of people attending both primary and specialized care services, and, of course, all this produces a sharp increase in healthcare expenditure. All this context makes health institutions to set a series of main objectives: prioritization of prevention, control of risk factors and early detection of diseases. To support primary prevention, it is important that health professionals have all the available means at their disposal to extract knowledge from main sources of information, that is, the patient’s electronic health records. Thus, health professionals should have tools that allow them to know and interrelate clinical events of interest, receive alerts about upcoming health risks or predict the development of a disease. However, the effort, time and cost required to extract this knowledge by just reading of the multiple clinical reports belonging to a patient's history (mostly written in natural language), are incalculable and hardly affordable for most health professionals in the daily clinic practice. Until now, the existing information systems in most health institutions have only been used as information storage systems, that is, systems that collect and store any healthcare information generated in the practitioner-patient interaction. By now, the step of transforming such raw data into useful "knowledge" that eases and supports the final clinical decision-making process has not been applied yet. Nevertheless, such challenge of transforming raw data into knowledge is not trivial. It is estimated that in a regional hospital more than 3 million clinical documents can be generated per year, 80% of them contain unstructured or textual information. Up to now, textual clinical information has been practically ignored by most health institutions mainly due to the arduous process required to take advantage of the content of such vast amount of data. Thus, the main source of knowledge contained in the electronic medical records, which is in textual clinical narrative, is practically untapped. Additionally to the difficulty of the health organizations to obtain value from the text by using traditional tools, the peculiar characteristics of the clinical terminology is an added problem: high ambiguity and complexity of the vocabulary, free textual narrative, a poor terminological standardization and an overuse of acronyms and negations. In this complex framework and in view of the growing need to acquire knowledge to support the decision-making process, it is essential to use Intelligent Systems that help to extract the value from textual documents. Currently, there are very few real systems able to extract knowledge from clinical texts and to really ease the daily work of healthcare professionals in complex tasks such as risk factor detection or diagnostic prediction. In recent years, to face these problems up, there are a number of advanced techniques provided by the Text Mining (TM) discipline. TM might be defined as an area focused on the identification and extraction of new knowledge from textual information, and it is seen as a multidisciplinary field gathering techniques from other disciplines such as Natural Language Processing (NLP) and Machine Learning (ML). In this sense, this doctoral Thesis first provides an exhaustive and detailed analysis of the state-of-the-art on the TM discipline in Medicine. This analysis includes the most outstanding methods, techniques, tasks, resources and trends in the field. As a result, this review revealed that the existing systems to support the clinical decision-making process by applying a textual clinical information are scarce, and they generally perform a single task on a specific area of knowledge and for very specific domains hardly applied to problems on different environments. In this regard, this Thesis proposes the development of a new system, called MiNerDoc, to support clinical decision-making by applying a combination of techniques from the TM discipline, along with the terminological and semantic enrichment provided by the MetaMap tool and the UMLS metathesaurus. MiNerDoc allows, among other functionalities, the detection of risk factors or clinical events of interest and automatic inference of standardized diagnostic codes based on the textual information included in clinical reports. The proposed TM system has been evaluated based on an extensive experimental study and the results have demonstrated the effectiveness and viability of such system in two tasks, recognition of medical entities and multi-label diagnostic classification