<p>This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology&nbsp / resented can be integrated with the researchers&rsquo / own knowledge, experimentation and observations for optimal progression of scientific research.</p>

Orientador: Regina Paolucci El Dib / Resumo: Introduction: A high-quality electronic search is essential in ensuring accuracy and comprehensivness in identifying potentially relevant records in conducting a systematic review. To assist researchers in identifying terms when formulating a sensitive search strategy, librarians and educators instruct researchers to consult and include preferred and non-preferred terms of the controlled database. However, by using all available terms in the thesaurus (i.e. subject headings), strategies can be lengthy and very laborious. Objective: To identify the most efficient method for searching in both Medline through PubMed and EMBASE, covering search terms with different spellings, direct and indirect orders, and association (or lack thereof) with MeSH and EMTREE terms. Method: In our cross-sectional study of search strategies, we selected and analysed 37 search strategies specifically developed for the anesthesiology field. These search strategies were adapted in order to cover all potentially relevant search terms in terms of different spellings and direct and indirect orders, most efficiently. Results: When adapted to include different spellings and direct and indirect orders, adapted versions of the selected search strategies retrieved the same number of search results in the Medline (mean of 61,3%) and higher number in EMBASE (mean of 63,9%) of the analyzed sample. The number of results retrieved by the searches analysed was not identical using the association or not of MeSH and E... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Philosophiae Doctor - PhD / This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology, resented can be integrated with the researchers own knowledge, experimentation and observations for optimal progression of scientific research. / South Africa

Recent genomic data indicate that RNA polymerase II (Pol II) function extends beyond conventional transcription of primarily protein-coding genes. Among the five snRNAs required for pre-mRNA splicing, only the U6 snRNA is synthesized by RNA polymerase III (Pol III). Here we address the question of how Pol II coordinates the expression of spliceosome components, including U6. We used chromatin immunoprecipitation (ChIP) and high-resolution mapping by PCR to localize both Pol II and Pol III to snRNA gene regions. We report the surprising finding that Pol II is highly concentrated ∼300 bp upstream of all five active human U6 genes in vivo. The U6 snRNA, an essential component of the spliceosome, is synthesized by Pol III, whereas all other spliceosomal snRNAs are Pol II transcripts. Accordingly, U6 transcripts were terminated in a Pol III-specific manner, and Pol III localized to the transcribed gene regions. However, synthesis of both U6 and U2 snRNAs was α-amanitin-sensitive, indicating a requirement for Pol II activity in the expression of both snRNAs. Moreover, both Pol II and histone tail acetylation marks were lost from U6 promoters upon α-amanitin treatment. The results indicate that Pol II is concentrated at specific genomic regions from which it can regulate Pol III activity by a general mechanism. Consequently, Pol II coordinates expression of all RNA and protein components of the spliceosome.

The diploma thesis examines biomedicine bibliographic databases MEDLINE and PubMed, both developed, maintained and provided by the U. S. National Library of Medicine (NLM). A general goal of thesis is offering answers to the question of historical and current importance of these resources. Text deals with a historical context of databases formation and their reflection in Czechoslovakian and Czech scientific community, what is also supported by qualitative research conducted as part of the thesis. Main part of the thesis deals with comprehensive analysis of the databases within environment formed by the NLM; analysis of systems components and PubMed graphic user interface and its query language. The thesis also presents issues of accessing databases content through third parties alternative platforms or interfaces built upon E-Utilities API. In the end, thesis offers ideas on probable development of MEDLINE and PubMed databases in the near and distant future. Powered by TCPDF (www.tcpdf.org)

The U.S. National Library of Medicine brought the intensifying interest in open science to national attention when it joined enthusiastic scientists to introduce and host an Amazon-like rating forum on PubMed—the world's largest database of indexed biomedical and life sciences literature. The result was PubMed Commons. In June 2013, the commenting forum was introduced for open discussion about published scientific literature as part of a three-pronged approach to improve research rigor, reproducibility, and transparency. In Feb. 2018, the forum was unexpectedly discontinued. This retrospective explanatory case study research asked the question, "What happened on the way to the forum?" Answers came from a variety of resources using multiple methodologies for data collection and analysis. Historical data from PubMed Commons' 7,629 comments and 1,551 commenters; key informant interviews with PubMed Commons editors; and a systematized search for published articles, gray literature; and social media content about PubMed Commons were analyzed using computer-mediated discourse analysis and a social network analysis. Results from the quantitative content analysis described a forum with little participation, and the qualitative content analysis demonstrated that active forum members were focused primarily on providing links to other information resources and discussing aspects of post-publication peer review. The social network analysis revealed a disconnected network, which was supported by a sociogram showing a community of independents with only seven small clusters. Findings pointed to 11 factors that affected the forum's adoption and use. Rogers' diffusion of innovation theory scaffolds a forum innovation agility model developed from this work to offer a better understanding of organizational processes and to aid organizations interested in introducing and managing a similar forum. PubMed Commons was a missed opportunity. No comparable alternative is available to promote open science and serve as a tool for the expected paradigm shift in the way we do scholarly communication in science.

Philosophiae Doctor - PhD / Advancement in biomedical research and continuous growth of scientific literature available in electronic form, calls for innovative methods and tools for information management, knowledge discovery, and data integration. Many biomedical fields such as genomics, proteomics, metabolomics, genetics, and emerging disciplines like systems biology and conceptual biology require synergy between experimental, computational, data mining and text mining technologies. A large amount of biomedical information available in various repositories, such as the US National Library of Medicine Bibliographic Database, emerge as a potential source of textual data for knowledge discovery. Text mining and its application of natural language processing and machine learning technologies to problems of knowledge discovery, is one of the most challenging fields in bioinformatics. This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology presented can be integrated with the researchers' own knowledge, experimentation and observations for optimal progression of scientific research.

Yes / Symptoms and signs (symptoms in brief) are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM). To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. The proposed method gets reliable gene rank list with AUC (area under curve) 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms. / NSFC Project (61105055, 81230086), China 973 Program (2014CB542903), The National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), the National S&T Major Special Project on Major New Drug Innovation (2012ZX09503-001-003), and the Fundamental Research Funds for the Central Universities.

Indiana University-Purdue University Indianapolis (IUPUI) / The literature corresponding to disordered proteins has been on a rise. As the number of publications increase, the time and effort needed to manually identify the relevant publications and protein information to add to centralized repository (called DisProt) is becoming arduous and critical. Existing search facilities on PubMed can retrieve a seemingly large number of publications based on keywords and does not have any support for ranking them based on the probability of the protein names mentioned in a given abstract being added to DisProt. This thesis explores a novel system of using disorder predictors and context based dictionary methods to quickly identify publications on disordered proteins from the PubMed database. NLProt, which is built around Support Vector Machines, is used to identify protein names and PONDR-FIT which is an Artificial Neural Network based meta- predictor is used for identifying protein disorder. The work done in this thesis is of immediate significance in identifying disordered protein names. We have tested the new system on 100 abstracts from DisProt [these abstracts were found to be relevant to disordered proteins and were added to DisProt manually by the annotators.] This system had an accuracy of 87% on this test set. We then took another 100 recently added abstracts from PubMed and ran our algorithm on them. This time it had an accuracy of 68%. We suggested improvements to increase the accuracy and believe that this system can be applied for identifying disordered proteins from literature.

自2003年布達佩斯宣言公佈起，國際間學術文獻開始開放取用的趨勢。於此背景下，本研究以諾貝爾生物醫學獎近十年23位得主為研究樣本，評比在商業資料庫(SCIE、Scopus)及開放取用系統(生物醫學類：Pubmed、Highwire；綜合類：Google Scholar)的文獻收錄狀況，除了比較其內部重複性與完整性，並交叉比對五個資料庫與系統的重複性、獨特性及完整性，同時也觀看能否取得全文的比率，來了解現今開放取用文獻的狀況，進而觀察開放取用系統和商業資料庫兩者是否可以互補，或是開放取用系統有代替商業資料庫的可能性。 研究結果顯示五個資料庫及系統的檢索形式多元。針對作者檢索而言，Scopus最完善，資料收錄也較齊全；SCIE及Pubmed兩者則是檢索結果最為相似。如果以學術出版收錄而言，則是Highwire較完整；至於Google Scholar的獨特性較高。整體而言，開放取用系統比商業資料庫的全文收錄比例高，但Scopus是收錄最多全文的資料庫。本研究同時也發現PNAS是五個資料庫與系統之重複來源及獨特來源。另外，使用PubMed及Highwire檢索生物醫學文獻會比Google Scholar來得專業。 根據研究結果建議，商業資料庫可考慮將網路開放資源納入收錄範圍，以便妥善整理及應用網路資源的書目及全文。開放取用系統則應改善索引書目之正確性及著錄完整性。另外，針對圖書館的服務宜採取以下之因應措施：(1)加強推廣商業資料庫之正確檢索方式及使用時機；(2)教導如何正確使用開放取用系統的檢索模式；(3)平衡商業資料庫和開放取用系統的使用，以達成圖書館經費的合理運用。 本研究後續可延伸至生物醫學領域的臨床及實證醫學上，以了解生物醫學中兩個最具時效性的學術文獻系統是否達到開放取用的立即性及實用性。再者，使用者對開放取用的滿意度研究是學術出版界急欲了解的課題，也是後續研究可加強努力的方向。 / The International Scholarly Communication has gradually forwarded open access system since the publication of Budapest Declaration in 2003. Under this research background, this study uses biomedical Nobel Prize winners in recent years for the study of 23 samples of appraisal in the commercial database (SCIE, Scopus) and open access systems (biomedical categories: Pubmed, Highwire ; Comprehensive: Google Scholar) literature collection status, in addition to comparing repeatability and integrity of its internal and cross-comparison of the five databases and system repeatability, uniqueness and integrity, while also viewing the ability to obtain the ratio of text to understand current status of open access literature, and then observe the open access systems and commercial databases whether the two can complement each other, or open access database system instead of commercial possibilities. The results showed that five databases have different retrieval systems in many different forms. For the purposes of retrieval, Scopus collections are more complete; SCIE and Pubmed are the most similar two databases in the search results. Inclusion academic publishing purposes, Highwire is the most complete one. For Google Scholar, the collection’s uniqueness is the highest. Overall, comparing the open access system with commercial database, open access system contains a high proportion of full text. Scopus is the most one of full text collections. The PNAS study also found that five of the duplicate database and system sources and unique source. In addition, the use of PubMed and Highwire retrieved biomedical literature is more professional than Google Scholar. According to the study results suggest that commercial databases can be considered included in the scope of network resources into the open, in order to properly organize network resources and application of bibliographic and full-text. Open access system should improve the accuracy and bibliographic indexing bibliographic completeness. In addition, for the library service should take the following measures in response to: (a) enhance the promotion of commercial database retrieval methods and the use of proper timing; (2) to teach the proper use of open access system retrieval mode; (3) Balance Business open access database and use of the system, in order to achieve rational use of library funds. The follow-up research of this study can be extended to the field of clinical and biomedical evidence-based medicine research. The follow-up research results can be used to understand the biomedical literature’ timeliness, whether the system reaches an open access immediate or practicality. Furthermore, users' satisfaction with open access scholarly publishing research is also an anxious subject to know, and the follow-up study will strengthen efforts.