Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16

Semantic Similarity in Cheminformatics

Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer

Recommender system to support comprehensive exploration of large scale scientific datasets

Bases de dados de entidades científicas, como compostos químicos, doenças e objetos astronómicos, têm crescido em tamanho e complexidade, chegando a milhares de milhões de itens por base de dados. Os investigadores precisam de ferramentas novas e inovadoras para auxiliar na escolha desses itens. Este trabalho propõe o uso de Sistemas de Recomendação para auxiliar os investigadores a encontrar itens de interesse. Identificamos como um dos maiores desafios para a aplicação de sistemas de recomendação em áreas científicas a falta de conjuntos de dados padronizados e de acesso aberto com informações sobre as preferências dos utilizadores. Para superar esse desafio, desenvolvemos uma metodologia denominada LIBRETTI - Recomendação Baseada em Literatura de Itens Científicos, cujo objetivo é a criação de conjuntos de dados , relacionados com campos científicos. Estes conjuntos de dados são criados com base no principal recurso de conhecimento que a Ciência possui: a literatura científica. A metodologia LIBRETTI permitiu o desenvolvimento de novos algoritmos de recomendação específicos para vários campos científicos. Além do LIBRETTI, as principais contribuições desta tese são conjuntos de dados de recomendação padronizados nas áreas de Astronomia, Química e Saúde (relacionado com a doença COVID-19), um sistema de recomendação semântica híbrido para compostos químicos em conjuntos de dados de grande escala, uma abordagem híbrida baseada no enriquecimento sequencial (SeEn) para recomendações sequenciais, um pipeline baseado em semântica de vários campos para recomendar entidades biomédicas relacionadas com a doença COVID-19.Databases for scientific entities, such as chemical compounds, diseases and astronomical objects, are growing in size and complexity, reaching billions of items per database. Researchers need new and innovative tools for assisting the choice of these items. This work proposes the use of Recommender Systems approaches for helping researchers to find items of interest. We identified as one of the major challenges for applying RS in scientific fields the lack of standard and open-access datasets with information about the preferences of the users. To overcome this challenge, we developed a methodology called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is to create datasets related to scientific fields. These datasets are created based on scientific literature, the major resource of knowledge that Science has. LIBRETTI methodology allowed the development and testing of new recommender algorithms specific for each field. Besides LIBRETTI, the main contributions of this thesis are standard and sequence-aware recommendation datasets in the fields of Astronomy, Chemistry, and Health (related to COVID-19 disease), a hybrid semantic recommender system for chemical compounds in large-scale datasets, a hybrid approach based on sequential enrichment (SeEn) for sequence-aware recommendations, a multi-field semantic-based pipeline for recommending biomedical entities related to COVID-19 disease

Development of a text mining approach to disease network discovery

Scientific literature is one of the major sources of knowledge for systems biology, in the form of papers, patents and other types of written reports. Text mining methods aim at automatically extracting relevant information from the literature. The hypothesis of this thesis was that biological systems could be elucidated by the development of text mining solutions that can automatically extract relevant information from documents. The first objective consisted in developing software components to recognize biomedical entities in text, which is the first step to generate a network about a biological system. To this end, a machine learning solution was developed, which can be trained for specific biological entities using an annotated dataset, obtaining high-quality results. Additionally, a rule-based solution was developed, which can be easily adapted to various types of entities. The second objective consisted in developing an automatic approach to link the recognized entities to a reference knowledge base. A solution based on the PageRank algorithm was developed in order to match the entities to the concepts that most contribute to the overall coherence. The third objective consisted in automatically extracting relations between entities, to generate knowledge graphs about biological systems. Due to the lack of annotated datasets available for this task, distant supervision was employed to train a relation classifier on a corpus of documents and a knowledge base. The applicability of this approach was demonstrated in two case studies: microRNAgene relations for cystic fibrosis, obtaining a network of 27 relations using the abstracts of 51 recently published papers; and cell-cytokine relations for tolerogenic cell therapies, obtaining a network of 647 relations from 3264 abstracts. Through a manual evaluation, the information contained in these networks was determined to be relevant. Additionally, a solution combining deep learning techniques with ontology information was developed, to take advantage of the domain knowledge provided by ontologies. This thesis contributed with several solutions that demonstrate the usefulness of text mining methods to systems biology by extracting domain-specific information from the literature. These solutions make it easier to integrate various areas of research, leading to a better understanding of biological systems

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

Entity Linking for the Biomedical Domain

Entity linking is the process of detecting mentions of different concepts in text documents and linking them to canonical entities in a target lexicon. However, one of the biggest issues in entity linking is the ambiguity in entity names. The ambiguity is an issue that many text mining tools have yet to address since different names can represent the same thing and every mention could indicate a different thing. For instance, search engines that rely on heuristic string matches frequently return irrelevant results, because they are unable to satisfactorily resolve ambiguity. Thus, resolving named entity ambiguity is a crucial step in entity linking. To solve the problem of ambiguity, this work proposes a heuristic method for entity recognition and entity linking over the biomedical knowledge graph concerning the semantic similarity of entities in the knowledge graph. Named entity recognition (NER), relation extraction (RE), and relationship linking make up a conventional entity linking (EL) system pipeline (RL). We have used the accuracy metric in this thesis. Therefore, for each identified relation or entity, the solution comprises identifying the correct one and matching it to its corresponding unique CUI in the knowledge base. Because KBs contain a substantial number of relations and entities, each with only one natural language label, the second phase is directly dependent on the accuracy of the first. The framework developed in this thesis enables the extraction of relations and entities from the text and their mapping to the associated CUI in the UMLS knowledge base. This approach derives a new representation of the knowledge base that lends it to the easy comparison. Our idea to select the best candidates is to build a graph of relations and determine the shortest path distance using a ranking approach. We test our suggested approach on two well-known benchmarks in the biomedical field and show that our method exceeds the search engine's top result and provides us with around 4% more accuracy. In general, when it comes to fine-tuning, we notice that entity linking contains subjective characteristics and modifications may be required depending on the task at hand. The performance of the framework is evaluated based on a Python implementation