München, Techn. Univ., Diss., 2004.

BibTeX gilt als leistungsfähiges Literaturverwaltungsprogramm für LaTeX. In der praktischen Anwendung erweist es sich jedoch als recht spröde und unhandlich. Verschiedene Ansätze versuchen, einige seiner Kanten abzuschleifen. In diesem Vortrag werden einige Tools erwähnt und vorgestellt, die das Leben mit BibTeX unter Unix, speziell Linux, erleichtern sollen.

Thesis (Ph. D.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Oct. 20, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Electrical and Computer Engineering, University of Alberta." Includes bibliographical references.

Biological sciences literature can be extraordinarily difficult to understand. Papers are commonly filled with terminology unique to a particular sub-discipline. Readers with expertise outside that sub-discipline often have difficulty understanding information the author is trying to convey. The WikiPDF project that is the subject of this thesis helps readers understand the biological sciences literature by automatically generating a customized glossary for each page of any technical paper available in Adobe Portable Document Format (PDF) format. WikiPDF relies on the Wikipedia®, an on-line encyclopedia created and supported by a host of volunteers, as a source of definitions used in its glossaries. WikiPDF uses the National Institutes of Health (NIH) Medline/PubMed database of journal papers to organize, index, and locate WikiPDF glossaries. Design and implementation of this project relied exclusively on open-source software, including the Linux operating system, the Apache Tomcat web server, and the MySQL relational database system.

Thesis (Ph. D.)--Portland State University, 1996. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves [157]-163).

Ph.D. / Medical Informatics and Clinical Epidemiology / Tools to automatically summarize gene information from the literature have the potential to help genomics researchers better interpret gene expression data and investigate biological pathways. Even though several useful human-curated databases of information about genes already exist, these have significant limitations. First, their construction requires intensive human labor. Second, curation of genes lags behind the rapid publication rate of new research and discoveries. Finally, most of the curated knowledge is limited to information on single genes. As such, most original and up-to-date knowledge on genes can only be found in the immense amount of unstructured, free text biomedical literature. Genomic researchers frequently encounter the task of finding information on sets of differentially expressed genes from the results of common highthroughput technologies like microarray experiments. However, finding information on a set of genes by manually searching and scanning the literature is a time-consuming and daunting task for scientists. For example, PubMed, the first choice of literature research for biologists, usually returns hundreds of references for a search on a single gene in reverse chronological order. Therefore, a tool to summarize the available textual information on genes could be a valuable tool for scientists. In this study, we adapted automatic summarization technologies to the biomedical domain to build a query-based, task-specific automatic summarizer of information on mouse genes studied in microarray experiments - mouse Gene Information Clustering and Summarization System (GICSS). GICSS first clusters a set of differentially expressed genes by Medical Subject Heading (MeSH), Gene Ontology (GO), and free text features into functionally similar groups;next it presents summaries for each gene as ranked sentences extracted from MEDLINE abstracts, with the ranking emphasizing the relation between genes, similarity to the function cluster it belongs to, and recency. GICSS is available as a web application with links to the PubMed (www.pubmed.gov) website for each extracted sentence. It integrates two related steps, functional gene clustering and gene information gathering, of the microarray data analysis process. The information from the clustering step was used to construct the context for summarization. The evaluation of the system was conducted with scientists who were analyzing their real microarray datasets. The evaluation results showed that GICSS can provide meaningful clusters for real users in the genomic research area. In addition, the results also indicated that presenting sentences in the abstract can provide more important information to the user than just showing the title in the default PubMed format. Both domain-specific and non-domain-specific terminologies contributed in the informative sentences selection. Summarization may serve as a useful tool to help scientists to access information at the time of microarray data analysis. Further research includes setting up the automatic update of MEDLINE records; extending and fine-tuning of the feature parameters for sentence scoring using the available evaluation data; and expanding GICSS to incorporate textual information from other species. Finally, dissemination and integration of GICSS into the current workflow of the microarray analysis process will help to make GICSS a truly useful tool for the targeted users, biomedical genomics researchers.

The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational polypharmacology. In this work, we introduce CASSANDRA, a fully corpus-based and unsupervised algorithm which uses the MEDLINE indexed titles and abstracts to infer drug gene associations and assist drug repositioning. CASSANDRA measures the Pointwise Mutual Information (PMI) between biomedical terms derived from Gene Ontology (GO) and Medical Subject Headings (MeSH). Based on the PMI scores, drug and gene profiles are generated and candidate drug gene associations are inferred when computing the relatedness of their profiles. Results show that an Area Under the Curve (AUC) of up to 0.88 can be achieved. The algorithm can successfully identify direct drug gene associations with high precision and prioritize them over indirect drug gene associations. Validation shows that the statistically derived profiles from literature perform as good as (and at times better than) the manually curated profiles. In addition, we examine CASSANDRA’s potential towards drug repositioning. For all FDA-approved drugs repositioned over the last 5 years, we generate profiles from publications before 2009 and show that the new indications rank high in these profiles. In summary, co-occurrence based profiles derived from the biomedical literature can accurately predict drug gene associations and provide insights onto potential repositioning cases.

The purpose of this paper is to compare the three search engines Web of Science, Google Scholar and MEDLINE in regards of recovery efficiency and the overlap of relevant documents when it comes to information searching for academic purposes. Furthermore, it raises the question whether freely available search engines and licensed search engines are interchangeable with each other. The empirical data in this study were collected through searches conducted in the three search engines Web of Science, Google Scholar and MEDLINE. Twenty search queries were used and the first twenty retrieved documents for each query were examined for relevance using previously designed criteria. The documents were scored by a binary relevancy scale and thereafter a precision value for each search engine was calculated. The overlap of retrieved relevant document in all three search engines were also calculated using Jaccard’s Index. The results of this study showed that Web of Science was the search engine that had the highest precision value, 0.346 and that the largest overlap was between MEDLINE and Web of Science with a value of 0.112. / Program: Bibliotekarie

Diss. (sammanfattning) Linköping : Univ., 2004. / Härtill 5 uppsatser.

Objective: This research explored how time-to-event dental articles were indexed and reported, and sought solutions to help improve the reporting and identification of these articles, so that they could be more easily found and used to inform practice and research. Methods: Articles reporting time-to-event dental outcomes in humans were identified from the 50 dental journals with the highest impact factor for 2008. These were handsearched, identifying 'case' articles (n=95), active controls (likely false positives, n=91), and passive controls (other true negatives, n=6796). The medical subject headings (MESH) that had been assigned to the articles in MEDLINE, and words used in titles and abstracts describing time-to-events were compared between the 'cases' and controls. Time-to-event words and figures within articles were also sought, and reporting quality was assessed. Search strategies to identify time-toevent articles were developed, using indexing terms and free-text words. An independent cohort of articles was used to validate the search strategies, consisting of 148 time-to-event articles handsearched from 6514 articles in the 50 dental journals with the highest impact factor for 2012. The findings of the research were used to draft guidance to improve reporting, which was circulated amongst 78 stakeholder experts for comment, and modified. Results: MeSH indexing of time-to-event analyses was inconsistent and inaccurate, author descriptions in abstracts and titles varied, and the quality of time-to-event reporting and graphics in the body of those articles was poor. The burden faced by someone wishing to find and use these articles was considered high. Sensitive, precise and optimized electronic search strategies were developed and validated with sensitivities up to 92% and precisions up to 93%. The draft guidance attracted comment from 46 experts across 15 countries, with approximately 90% of the 130 comments accepted into the revised version. The importance of good quality reporting was endorsed, and there was high interest in commending the guidance to authors, reviewers, and training dental specialists. Conclusions: This research programme explored how time-to-event dental articles were reported, and used those findings to suggest solutions that would help to improve the identification and use of these data, reducing research waste.