Plataforma colaborativa de anotação de literatura biomédica

Mestrado em Engenharia de Computadores e TelemáticaWith the overwhelming amount of biomedical textual information being produced, several manual curation efforts have been set up to extract and store concepts and their relationships into structured resources. Since manual annotation is a very demanding and expensive task, computerized solutions were developed to perform such tasks automatically. Nevertheless, high-end information extraction techniques are still not widely used by biomedical research communities, mainly due to the lack of standards and limitations in usability. Interactive annotation tools intend to fill this gap, taking advantage of automatic techniques and existing knowledge bases to assist expert curators in their daily tasks. This thesis presents Egas, a web-based platform for biomedical text mining and assisted curation with highly usable interfaces for manual and automatic inline annotation of concepts and relations. Furthermore, a comprehensive set of knowledge bases are integrated and indexed to provide straightforward concept normalization features. Additionally, curators can also rely on real-time collaboration and conversation functionalities allowing discussing details of the annotation task as well as providing instant feedback of curators interactions. Egas also provides interfaces for on-demand management of the annotation task settings and guidelines, and supports standard formats and literature services to import and export documents. By taking advantage of Egas, we participated in the BioCreative IV interactive annotation task, targeting the assisted identification of protein-protein interactions described in PubMed abstracts related to neuropathological disorders. Thereby, when evaluated by expert curators, Egas obtained very positive scores in terms of usability, reliability and performance. These results, together with the provided innovative features, place Egas as a state-of-the-art solution for fast and accurate curation of information, facilitating the task of creating and updating knowledge bases in a more consistent way.Com o acréscimo da quantidade de literatura biomédica a ser produzida todos os dias, vários esforços têm sido feitos para tentar extrair e armazenar de forma estruturada os conceitos e as relações nela presentes. Por outro lado, uma vez que a extração manual de conceitos compreende uma tarefa extremamente exigente e exaustiva, algumas soluções de anotação automática foram surgindo. No entanto, mesmo os sistemas de anotação mais completos não têm sido muito bem recebidos no seio das equipas de investigação, em grande parte devido às falhas a nível de usabilidade e de interface standards. Para colmatar esta falha são necessárias ferramentas de anotação interativa, que tirem proveito de sistemas de anotação automática e de bases de dados já existentes, para ajudar os anotadores nas suas tarefas do dia-a-dia. Nesta dissertação iremos apresentar uma plataforma de anotação de literatura biomédica orientada para a usabilidade e que suporta anotação manual e automática. No mesmo sentido, integramos no sistema várias bases de dados, no intuito de facilitar a normalização dos conceitos anotados. Por outro lado, os utilizadores podem também contar com funcionalidades colaborativas em toda a aplicação, estimulando assim a interação entre os anotadores e, desta forma, a produção de melhores resultados. O sistema apresenta ainda funcionalidades para importar e exportar ficheiros, gestão de projetos e diretivas de anotação. Com esta plataforma, Egas, participámos na tarefa de anotação interativa do BioCreative IV (IAT), nomeadamente na identificação de interações proteína-proteína. Depois de avaliado por um conjunto de anotadores, o Egas obteve os melhores resultados entre os sistemas apresentados, relativamente à usabilidade, confiança e desempenho

Semi-automatische Verschlagwortung zur Integration externer semantischer Inhalte innerhalb einer medizinischen Kooperationsplattform

PubMed stellt mit 21 Mio. Aufsatzzitaten eines der umfangreichsten Informationssysteme in Bereich der Medizin. Durch die Verwendung einer einheitlichen Terminologie (Medical Subject Heading - MeSH) bei der Indizierung von PubMed Inhalten kann die Orientierung in solch großen Datenbeständen optimiert werden. Zwar bietet ein kontrolliertes Vokabular bei der Informationsbeschaffung zahlreiche Vorteile gegenüber einer Freitextsuche doch fällt Nutzern das Abbilden eines Informationsbedarfs auf die verwendete Terminologie oftmals schwer. In dieser Arbeit wird eine Systemunterstützung geschaffen, die den Abbildungsprozess automatisiert indem eine automatische Verschlagwortung textbasierter Inhalte unter Verwendung eines kontrollierten Vokabulars vorgenommen wird. Durch die Verwendung einer einheitliche Terminologie kann so eine konsistente Integration von PubMed Inhalten erreicht werden

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Systematising and scaling literature curation for genetically determined developmental disorders

The widespread availability of genomic sequencing has transformed the diagnosis of genetically-determined developmental disorders (GDD). However, this type of test often generates a number of genetic variants, which have to be reviewed and related back to the clinical features (phenotype) of the individual being tested. This frequently entails a time-consuming review of the peer-reviewed literature to look for case reports describing variants in the gene(s) of interest. This is particularly true for newly described and/or very rare disorders not covered in phenotype databases. Therefore, there is a need for scalable, automated literature curation to increase the efficiency of this process. This should lead to improvements in the speed in which diagnosis is made, and an increase in the number of individuals who are diagnosed through genomic testing. Phenotypic data in case reports/case series is not usually recorded in a standardised, computationally-tractable format. Plain text descriptions of similar clinical features may be recorded in several different ways. For example, a technical term such as ‘hypertelorism’, may be recorded as its synonym ‘widely spaced eyes’. In addition, case reports are found across a wide range of journals, with different structures and file formats for each publication. The Human Phenotype Ontology (HPO) was developed to store phenotypic data in a computationally-accessible format. Several initiatives have been developed to link diseases to phenotype data, in the form of HPO terms. However, these rely on manual expert curation and therefore are not inherently scalable, and cannot be updated automatically. Methods of extracting phenotype data from text at scale developed to date have relied on abstracts or open access papers. At the time of writing, Europe PubMed Central (EPMC, https://europepmc.org/) contained approximately 39.5 million articles, of which only 3.8 million were open access. Therefore, there is likely a significant volume of phenotypic data which has not been used previously at scale, due to difficulties accessing non-open access manuscripts. In this thesis, I present a method for literature curation which can utilise all relevant published full text through a newly developed package which can download almost all manuscripts licenced by a university or other institution. This is scalable to the full spectrum of GDD. Using manuscripts identified through manual literature review, I use a full text download pipeline and NLP (natural language processing) based methods to generate disease models. These are comprised of HPO terms weighted according to their frequency in the literature. I demonstrate iterative refinement of these models, and use a custom annotated corpus of 50 papers to show the text mining process has high precision and recall. I demonstrate that these models clinically reflect true disease expressivity, as defined by manual comparison with expert literature reviews, for three well-characterised GDD. I compare these disease models to those in the most commonly used genetic disease phenotype databases. I show that the automated disease models have increased depth of phenotyping, i.e. there are more terms than those which are manually-generated. I show that, in comparison to ‘real life’ prospectively gathered phenotypic data, automated disease models outperform existing phenotype databases in predicting diagnosis, as defined by increased area under the curve (by 0.05 and 0.08 using different similarity measures) on ROC curve plots. I present a method for automated PubMed search at scale, to use as input for disease model generation. I annotated a corpus of 6500 abstracts. Using this corpus I show a high precision (up to 0.80) and recall (up to 1.00) for machine learning classifiers used to identify manuscripts relevant to GDD. These use hand-picked domain-specific features, for example utilising specific MeSH terms. This method can be used to scale automated literature curation to the full spectrum of GDD. I also present an analysis of the phenotypic terms used in one year of GDD-relevant papers in a prominent journal. This shows that use of supplemental data and parsing clinical report sections from manuscripts is likely to result in more patient-specific phenotype extraction in future. In summary, I present a method for automated curation of full text from the peer-reviewed literature in the context of GDD. I demonstrate that this method is robust, reflects clinical disease expressivity, outperforms existing manual literature curation, and is scalable. Applying this process to clinical testing in future should improve the efficiency and accuracy of diagnosis

Agile in-litero experiments:how can semi-automated information extraction from neuroscientific literature help neuroscience model building?

In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles in peer-reviewed journals. One challenge for modern neuroinformatics is to design methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and its integration into computational models. In this thesis, we introduce novel natural language processing (NLP) models and systems to mine the neuroscientific literature. In addition to in vivo, in vitro or in silico experiments, we coin the NLP methods developed in this thesis as in litero experiments, aiming at analyzing and making accessible the extended body of neuroscientific literature. In particular, we focus on two important neuroscientific entities: brain regions and neural cells. An integrated NLP model is designed to automatically extract brain region connectivity statements from very large corpora. This system is applied to a large corpus of 25M PubMed abstracts and 600K full-text articles. Central to this system is the creation of a searchable database of brain region connectivity statements, allowing neuroscientists to gain an overview of all brain regions connected to a given region of interest. More importantly, the database enables researcher to provide feedback on connectivity results and links back to the original article sentence to provide the relevant context. The database is evaluated by neuroanatomists on real connectomics tasks (targets of Nucleus Accumbens) and results in significant effort reduction in comparison to previous manual methods (from 1 week to 2h). Subsequently, we introduce neuroNER to identify, normalize and compare instances of identify neuronsneurons in the scientific literature. Our method relies on identifying and analyzing each of the domain features used to annotate a specific neuron mention, like the morphological term 'basket' or brain region 'hippocampus'. We apply our method to the same corpus of 25M PubMed abstracts and 600K full-text articles and find over 500K unique neuron type mentions. To demonstrate the utility of our approach, we also apply our method towards cross-comparing the NeuroLex and Human Brain Project (HBP) cell type ontologies. By decoupling a neuron mention's identity into its specific compositional features, our method can successfully identify specific neuron types even if they are not explicitly listed within a predefined neuron type lexicon, thus greatly facilitating cross-laboratory studies. In order to build such large databases, several tools and infrastructureslarge-scale NLP were developed: a robust pipeline to preprocess full-text PDF articles, as well as bluima, an NLP processing pipeline specialized on neuroscience to perform text-mining at PubMed scale. During the development of those two NLP systems, we acknowledged the need for novel NLP approaches to rapidly develop custom text mining solutions. This led to the formalization of the agile text miningagile text-mining methodology to improve the communication and collaboration between subject matter experts and text miners. Agile text mining is characterized by short development cycles, frequent tasks redefinition and continuous performance monitoring through integration tests. To support our approach, we developed Sherlok, an NLP framework designed for the development of agile text mining applications

Event extraction from biomedical texts using trimmed dependency graphs

This thesis explores the automatic extraction of information from biomedical publications. Such techniques are urgently needed because the biosciences are publishing continually increasing numbers of texts. The focus of this work is on events. Information about events is currently manually curated from the literature by biocurators. Biocuration, however, is time-consuming and costly so automatic methods are needed for information extraction from the literature. This thesis is dedicated to modeling, implementing and evaluating an advanced event extraction approach based on the analysis of syntactic dependency graphs. This work presents the event extraction approach proposed and its implementation, the JReX (Jena Relation eXtraction) system. This system was used by the University of Jena (JULIE Lab) team in the "BioNLP 2009 Shared Task on Event Extraction" competition and was ranked second among 24 competing teams. Thereafter JReX was the highest scorer on the worldwide shared U-Compare event extraction server, outperforming the competing systems from the challenge. This success was made possible, among other things, by extensive research on event extraction solutions carried out during this thesis, e.g., exploring the effects of syntactic and semantic processing procedures on solving the event extraction task. The evaluations executed on standard and community-wide accepted competition data were complemented by real-life evaluation of large-scale biomedical database reconstruction. This work showed that considerable parts of manually curated databases can be automatically re-created with the help of the event extraction approach developed. Successful re-creation was possible for parts of RegulonDB, the world's largest database for E. coli. In summary, the event extraction approach justified, developed and implemented in this thesis meets the needs of a large community of human curators and thus helps in the acquisition of new knowledge in the biosciences