Biblioteca Digital

In this paper we present ASAP - Automated Search with Agents in PubMed, a web-based service aiming to manage and automate scientific literature search in the PubMed database. The system allows the creation and management of web agents, specifically parameterized thematically and functionally, that automatically and periodically crawl the PubMed database, oriented to search and retrieve relevant results according the requirements provided by the user. The results, containing the publications list retrieved, are emailed to the agent owner weekly during the activity period programmed for the web agent. The ASAP service is devoted to help researchers, especially from the field of biomedicine and bioinformatics, in order to increase their productivity, and can be accessed at: http://esa.ipb.pt/~agentes.

In this paper we present ASAP - Automated Search with Agents in PubMed, a web-based service aiming to manage and automate scientific literature search in the PubMed database. The system allows the creation and management of web agents, parameterized thematically and functionally, that crawl the PubMed database autonomously and periodically, aiming to search and retrieve relevant results according the requirements provided by the user. The results, containing the publications list retrieved, are emailed to the agent owner on a weekly basis, during the activity period defined for the web agent. The ASAP service is devoted to help researchers, especially from the field of biomedicine and bioinformatics, in order to increase their productivity, and can be accessed at: http://esa.ipb.pt/~agentes.

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); BackgroundA high-quality electronic search is essential in ensuring accuracy and completeness in retrieved records for the conducting of a systematic review.ObjectiveWe analysed the available sample of search strategies to identify the best method for searching in Medline through PubMed, considering the use or not of parenthesis, double quotation marks, truncation and use of a simple search or search history.MethodsIn our cross-sectional study of search strategies, we selected and analysed the available searches performed during evidence-based medicine classes and in systematic reviews conducted in the Botucatu Medical School, UNESP, Brazil.ResultsWe analysed 120 search strategies. With regard to the use of phrase searches with parenthesis, there was no difference between the results with and without parenthesis and simple searches or search history tools in 100% of the sample analysed (P = 1.0). The number of results retrieved by the searches analysed was smaller using double quotations marks and using truncation compared with the standard strategy (P = 0.04 and P = 0.08, respectively).ConclusionsThere is no need to use phrase-searching parenthesis to retrieve studies; however...

This paper presents a topic map approach to PubMed in order to create a knowledge representation for this information system. PubMed is a free search engine that gives very full coverage of the related biomedical sciences. With more than 17 millions of citations since 1865, PubMed users have several problems to find the papers desired. So, it is necessary to organize these concepts in a semantic network. To achieve this objective, we use the Metamorphosis system, choosing the keywords from MeSH ontology. This way, we obtain an ontological index for PubMed, making easier to find specific papers.

This paper presents a topic map approach to PubMed in order to create a knowledge representation for this information system. PubMed is a free search engine that gives very full coverage of the related biomedical sciences. With more than 17 millions of citations since 1865, PubMed users have several problems to find the papers desired. So, it is necessary to organize these concepts in a semantic network. To achieve this objective, we use the Metamorphosis system, choosing the keywords from MeSH ontology. This way, we obtain an ontological index for PubMed, making easier to find specific papers.

Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction and analysis of genome-wide gene networks. The large body of biomedical literature is an important source of gene/protein interaction information. Recent advances in text mining tools have made it possible to automatically extract such documented interactions from free-text literature. In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools. Our proposed framework consists of analyses of the network topology, network topology-gene function relationship, and temporal network evolution to distill valuable information embedded in the gene functional interactions in literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that literature-based P53 networks exhibit small-world and scale-free properties. We also found that high degree genes in the literature-based networks have a high probability of appearing in the manually curated database and genes in the same pathway tend to form local clusters in our literature-based networks. Temporal analysis showed that genes interacting with many other genes tend to be involved in a large number of newly discovered interactions.

It is known that users of internet search engines often enter queries with misspellings in one or more search terms. Several web search engines make suggestions for correcting misspelled words, but the methods used are proprietary and unpublished to our knowledge. Here we describe the methodology we have developed to perform spelling correction for the PubMed search engine. Our approach is based on the noisy channel model for spelling correction and makes use of statistics harvested from user logs to estimate the probabilities of different types of edits that lead to misspellings. The unique problems encountered in correcting search engine queries are discussed and our solutions are outlined.

PubMedAlertMe is a Windows-based software system for automatically receiving e-mail alert messages about recent publications listed on PubMed. The e-mail messages contain links to newly available abstracts listed on PubMed describing publications that were selectively returned from a specified list of queries. Links are also provided to directly export citations to EndNote, and links are provided to directly forward articles to colleagues. The program is standalone. Thus, it does not require a remote mail server or user registration. PubMedAlertMe is free software, and can be downloaded from: http://amp.pharm.mssm.edu/PubMedAlertMe/PubMedAlertMe_setup.zip

Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed®, the public gateway to the MEDLINE® database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users’ search experience.

This paper investigates the effectiveness of using MeSH® in PubMed through its automatic query expansion process: Automatic Term Mapping (ATM). We run Boolean searches based on a collection of 64 topics and about 160,000 MEDLINE® citations used in the 2006 and 2007 TREC Genomics Tracks. For each topic, we first automatically construct a query by selecting keywords from the question. Next, each query is expanded by ATM, which assigns different search tags to terms in the query. Three search tags: [MeSH Terms], [Text Words], and [All Fields] are chosen to be studied after expansion because they all make use of the MeSH field of indexed MEDLINE citations. Furthermore, we characterize the two different mechanisms by which the MeSH field is used. Retrieval results using MeSH after expansion are compared to those solely based on the words in MEDLINE title and abstracts. The aggregate retrieval performance is assessed using both F-measure and mean rank precision. Experimental results suggest that query expansion using MeSH in PubMed can generally improve retrieval performance, but the improvement may not affect end PubMed users in realistic situations.

PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users’ search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping.

In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction.

Translational Science Search (http://tscience.nlm.nih.gov) is a Web application for finding MEDLINE/PubMed journal articles that are regarded by their authors as novel, promising, or may have potential clinical application. A set of “translational” filters and related terms was created by reviewing journal articles published in clinical and translational science journals. Through E-Utilities, a user’s query and translational science (TS) filters are submitted to PubMed, and then the retrieved PubMed citations are matched with a database of MeSH terms (for disease conditions) and RxNorm (for interventions) to locate the search term, translational filters found, and associated interventions in the title and abstract. An algorithm ranks the Interventions and Conditions, and then highlights them in the results page for quick reading and evaluation. Using previously searched terms and standard formulas, the Precision and Recall of Translational Science Search (TSS) were 0.99 and 0.47, compared to 0.58 and 1.0 for PubMed Entrez, respectively.

Many recent studies on MEDLINE-based information seeking have shed light on scientists’ behaviors and associated tool innovations that may improve efficiency and effectiveness. Few if any studies, however, examine scientists’ problem-solving uses of PubMed in actual contexts of work and corresponding needs for better tool support. Addressing this gap, we conducted a field study of novice scientists (14 upper level undergraduate majors in molecular biology) as they engaged in a problem solving activity with PubMed in a laboratory setting. Findings reveal many common stages and patterns of information seeking across users as well as variations, especially variations in cognitive search styles. Based on findings, we suggest tool improvements that both confirm and qualify many results found in other recent studies. Our findings highlight the need to use results from context-rich studies to inform decisions in tool design about when to offer improved features to users.

peer-reviewed; Like most medical doctors, we have searched for information on how to care for patients. As students, you may wish to search to answer a question, or to obtain background information for a project or presentation (and once you are on call, we assure you that you will be searching at 3am for evidence supporting your next option in treating a patient). Most of us with an unanswered question will ask colleagues or local experts but when they are uncertain or differ then it is appropriate to search published research for solutions. MEDLINE is most commonly used, and can be accessed via PubMed (www.pubmed.com).

Objectives In April 2010, the Université de Montréal’s Health Sciences Library has implemented shared filters in its institutional PubMed account. Most of these filters are designed to highlight resources for evidence-based practice, such as Clinical Queries, Systematic Reviews and Evidence-based Synopsis. We now want to measure how those filters are perceived and used by our users. Methods For one month, data was gathered through an online questionnaire proposed to users of Université de Montréal’s PubMed account. A print version was also distributed to participants in information literacy workshops given by the health sciences librarians. Respondents were restricted to users affiliated to Université de Montréal’s faculties of Medicine, Dentistry, Veterinary Sciences, Nursing and Pharmacy. Basic user information such as year/program of study or department affiliation was also collected. The questionnaire allowed users to identify the filters they use, assess the relevance of filters, and also suggest new ones. Results Survey results showed that the shared filters of Université de Montreal’s PubMed account were found useful by the majority of respondents. Filters allowing rapid access to secondary resources ranked among the most relevant (Reviews...

El Servei de Biblioteques disposa d’una URL específica per als usuaris de la Universitat Autònoma de Barcelona que permet enllaçar el resultat de les cerques de PubMed amb els articles i els serveis de les biblioteques de la UAB.

Objetivo: Analizar la producción científica española sobre obesidad en PubMed durante dos décadas (1988-2007) y contextualizarla en el marco de la Unión Europea. Métodos: Se realizó un estudio bibliométrico a través de la base de datos MEDLINE en PubMed mediante la búsqueda de «obesity» en MeSH, estimando las tendencias del número de publicaciones anuales mediante análisis de regresión. Resultados: Se recopilaron 50.120 documentos sobre obesidad, y 1.407 (2,8% de la producción mundial) eran de autores españoles. España fue el quinto país con más documentos de la Unión Europea (8,1%). El 69,1% (n=932) estaban publicados en inglés. El crecimiento medio anual fue del 15%, pasando de 91 documentos en el primer quinquenio a 702 en el último. La especialidad más frecuente del primer firmante fue endocrinología, con 279 (22,8%) documentos, seguida de fisiología-nutrición-bromatología con 203 (16,6%) y bioquímica con 161 (13,2%). Medicina Clínica con 83 (6,1%), Obesity Surgery con 79 (5,8%), International Journal of Obesity con 73 (5,3%) y Nutrición Hospitalaria con 59 (4,3%) fueron las revistas con mayor número de documentos. Las comunidades autónomas más productoras fueron Cataluña (n=338; 24,9%), Madrid (n=286; 20...

Objetivo: Analizar la producción científica española sobre obesidad en PubMed durante dos décadas (1988-2007) y contextualizarla en el marco de la Unión Europea. Métodos: Se realizó un estudio bibliométrico a través de la base de datos MEDLINE en PubMed mediante la búsqueda de «obesity» en MeSH, estimando las tendencias del número de publicaciones anuales mediante análisis de regresión. Resultados: Se recopilaron 50.120 documentos sobre obesidad, y 1.407 (2,8% de la producción mundial) eran de autores españoles. España fue el quinto país con más documentos de la Unión Europea (8,1%). El 69,1% (n=932) estaban publicados en inglés. El crecimiento medio anual fue del 15%, pasando de 91 documentos en el primer quinquenio a 702 en el último. La especialidad más frecuente del primer firmante fue endocrinología, con 279 (22,8%) documentos, seguida de fisiología-nutrición-bromatología con 203 (16,6%) y bioquímica con 161 (13,2%). Medicina Clínica con 83 (6,1%), Obesity Surgery con 79 (5,8%), International Journal of Obesity con 73 (5,3%) y Nutrición Hospitalaria con 59 (4,3%) fueron las revistas con mayor número de documentos. Las comunidades autónomas más productoras fueron Cataluña (n=338; 24,9%), Madrid (n=286; 20...

OBJECTIVES: To compare the information obtained from the Medline database using Internet commercial search engines with that obtained from a compact disc (Medline-CD). METHODS: An agreement study was carried out based on 101 clinical scenarios provided by specialists in internal medicine, pharmacy, gynaecology-obstetrics, surgery and paediatrics. 175 search strategies were employed using the connector AND plus text within quotation marks. The search was limited to 1991-1999. Internet search-engines were selected by common criteria. Identical search strategies were independently applied to and masked from Internet search engines, as well as the Medline-CD. RESULTS: 3,488 articles were obtained using 129 search strategies. Agreement with the Medline-CD was 54% for PubMed, 57% for Gateway, 54% for Medscape and 65% for BioMedNet. The highest agreement rate for a given speciality (paediatrics) was 78,1% for BioMedNet, having greater -/- than +/+ agreement. CONCLUSIONS: Even though free access to Medline has encouraged the boom and growth of evidence-based medicine, these results must be considered within the context of which search engine was selected for doing the searches. The internet search engines studied showed a poor agreement with the Medline-CD...