The TREC 2004 genomics track categorization task: classifying full text biomedical documents

BACKGROUND: The TREC 2004 Genomics Track focused on applying information retrieval and text mining techniques to improve the use of genomic information in biomedicine. The Genomics Track consisted of two main tasks, ad hoc retrieval and document categorization. In this paper, we describe the categorization task, which focused on the classification of full-text documents, simulating the task of curators of the Mouse Genome Informatics (MGI) system and consisting of three subtasks. One subtask of the categorization task required the triage of articles likely to have experimental evidence warranting the assignment of GO terms, while the other two subtasks were concerned with the assignment of the three top-level GO categories to each paper containing evidence for these categories. RESULTS: The track had 33 participating groups. The mean and maximum utility measure for the triage subtask was 0.3303, with a top score of 0.6512. No system was able to substantially improve results over simply using the MeSH term Mice. Analysis of significant feature overlap between the training and test sets was found to be less than expected. Sample coverage of GO terms assigned to papers in the collection was very sparse. Determining papers containing GO term evidence will likely need to be treated as separate tasks for each concept represented in GO, and therefore require much denser sampling than was available in the data sets. The annotation subtask had a mean F-measure of 0.3824, with a top score of 0.5611. The mean F-measure for the annotation plus evidence codes subtask was 0.3676, with a top score of 0.4224. Gene name recognition was found to be of benefit for this task. CONCLUSION: Automated classification of documents for GO annotation is a challenging task, as was the automated extraction of GO code hierarchies and evidence codes. However, automating these tasks would provide substantial benefit to biomedical curation, and therefore work in this area must continue. Additional experience will allow comparison and further analysis about which algorithmic features are most useful in biomedical document classification, and better understanding of the task characteristics that make automated classification feasible and useful for biomedical document curation. The TREC Genomics Track will be continuing in 2005 focusing on a wider range of triage tasks and improving results from 2004

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

Identifying chemical entities on literature:a machine learning approach using dictionaries as domain knowledge

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2013The volume of life science publications, and therefore the underlying biomedical knowledge, are growing at a fast pace. However the manual literature analysis is a slow and painful task. Hence, text mining systems have been developed to automatically locate the relevant information contained in the literature. An essential step in text mining is named entitiy recognition, but the inherent complexity of biomedical entities, such as chemical compounds, makes it difficult to obtain good performances in this task. This thesis proposes methods capable to improve the current performance of chemical entity recognition from text. Hereby a case based method for recognizing chemical entities is proposed and the obtained evaluation results outperform the most widely used methods, based in dictionaries. A lexical similarity based chemical entity resolution method was also developed and allows an efficient mapping of the recognized entities to the ChEBI database. To improve the chemical entity identification results we developed a validation method that exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text, in order to discriminate between the correctly identified entities that can be validated and identification errors that should be discarded. A machine learning method for entity recognition error is also proposed, which can efectively find recognition errors in rule based systems. The methods were integrated in a system capable of recognizing chemical entities in texts, map them to the ChEBI database, and provide evidence of validation or recognition error for the recognized entities.O volume de publicações científicas nas ciências da vida está a aumentar a um ritmo crescente. Contudo a análise manual da literatura é um processo árduo e moroso, pelo que têm sido desenvolvidos sistemas de prospecção de texto para identificar automaticamente a informação relevante contida na literatura. Um passo essencial em prospecção de texto é a identificação de entidades nomeadas, mas a complexidade inerente às entidades biomédicas, como é o caso dos compostos químicos, torna difícil obter bons desempenhos nesta tarefa. Esta tese propõe métodos para melhorar o desempenho actual do processo de reconhecimento de entidades químicas em texto. Para tal propõe-se um método para reconhecimento de entidades químicas baseado em aprendizagem automática, que obteve resultados superiores aos métodos baseados em dicionários utilizados actualmente. Desenvolveu-se ainda um método baseado em semelhança lexical que realiza o mapeamento de entidades para a ontologia ChEBI. Para melhorar os resultados de identificação de entidades químicas desenvolveu-se um método de validação que explora as relações semânticas do ChEBI para medir a semelhança entre as entidades encontradas no texto, de forma a discriminar as entidades correctamente identificadas dos erros de identificação. Um método de filtragem de erros baseado em aprendizagem automática é também proposto, e foi testado num sistema baseado em regras. Estes métodos foram integrados num sistema capaz de reconhecer as entidades químicas em texto, mapear para o ChEBI, e fornecer evidência para validação ou detecção de erros das entidades reconhecidas.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/36015/2007

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Identifying Relevant Evidence for Systematic Reviews and Review Updates

Systematic reviews identify, assess and synthesise the evidence available to answer complex research questions. They are essential in healthcare, where the volume of evidence in scientific research publications is vast and cannot feasibly be identified or analysed by individual clinicians or decision makers. However, the process of creating a systematic review is time consuming and expensive. The pace of scientific publication in medicine and related fields also means that evidence bases are continually changing and review conclusions can quickly become out of date. Therefore, developing methods to support the creating and updating of reviews is essential to reduce the workload required and thereby ensure that reviews remain up to date. This research aims to support systematic reviews, thus improving healthcare through natural language processing and information retrieval techniques. More specifically, this thesis aims to support the process of identifying relevant evidence for systematic reviews and review updates to reduce the workload required from researchers. This research proposes methods to improve studies ranking for systematic reviews. In addition, this thesis describes a dataset of systematic review updates in the field of medicine created using 25 Cochrane reviews. Moreover, this thesis develops an algorithm to automatically refine the Boolean query to improve the identification of relevant studies for review updates. The research demonstrates that automating the process of identifying relevant evidence can reduce the workload of conducting and updating systematic reviews

The Gene Ontology Handbook

bioinformatics; biotechnolog