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The purpose of this study was to investigate differences between abstracts of posters presented at the 79(th) (2002) and 80(th) (2003) Annual Session & Exhibition of the American Dental Education Association (ADEA) and the published full-length articles resulting from the same studies. The abstracts for poster presentation sessions were downloaded, and basic characteristics of the abstracts and their authors were determined. A PubMed search was then performed to identify the publication of full-length articles based on those abstracts in a peer-reviewed journal. The differences between the abstract and the article were examined and categorized as major and minor differences. Differences identified included authorship, title, materials and methods, results, conclusions, and funding. Data were analyzed with both descriptive and analytic statistics. Overall, 89 percent of the abstracts had at least one variation from its corresponding article, and 65 percent and 76 percent of the abstracts had at least one major and minor variation, respectively, from its corresponding article. The most prevalent major variation was in study results, and the most prevalent minor variation was change in the number of authors. The discussion speculates on some possible reasons for these differences.
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This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.
The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server. 2b1af7f3a8