61,251 research outputs found
Better duplicate detection for systematic reviewers: Evaluation of Systematic Review Assistant-Deduplication Module
BACKGROUND: A major problem arising from searching across bibliographic databases is the retrieval of duplicate citations. Removing such duplicates is an essential task to ensure systematic reviewers do not waste time screening the same citation multiple times. Although reference management software use algorithms to remove duplicate records, this is only partially successful and necessitates removing the remaining duplicates manually. This time-consuming task leads to wasted resources. We sought to evaluate the effectiveness of a newly developed deduplication program against EndNote. METHODS: A literature search of 1,988 citations was manually inspected and duplicate citations identified and coded to create a benchmark dataset. The Systematic Review Assistant-Deduplication Module (SRA-DM) was iteratively developed and tested using the benchmark dataset and compared with EndNoteâs default one step auto-deduplication process matching on (âauthorâ, âyearâ, âtitleâ). The accuracy of deduplication was reported by calculating the sensitivity and specificity. Further validation tests, with three additional benchmarked literature searches comprising a total of 4,563 citations were performed to determine the reliability of the SRA-DM algorithm. RESULTS: The sensitivity (84%) and specificity (100%) of the SRA-DM was superior to EndNote (sensitivity 51%, specificity 99.83%). Validation testing on three additional biomedical literature searches demonstrated that SRA-DM consistently achieved higher sensitivity than EndNote (90% vs 63%), (84% vs 73%) and (84% vs 64%). Furthermore, the specificity of SRA-DM was 100%, whereas the specificity of EndNote was imperfect (average 99.75%) with some unique records wrongly assigned as duplicates. Overall, there was a 42.86% increase in the number of duplicates records detected with SRA-DM compared with EndNote auto-deduplication. CONCLUSIONS: The Systematic Review Assistant-Deduplication Module offers users a reliable program to remove duplicate records with greater sensitivity and specificity than EndNote. This application will save researchers and information specialists time and avoid research waste. The deduplication program is freely available online
Good images, effective messages? Working with students and educators on academic practice understanding
Work at Northumbria University has focussed on activity that extends opportunities for students to engage directly with the skills development necessary for sound academic practice. This has included highly visual campaigns on the "Plagiarism trap", providing access to Turnitin plagiarism detection software, guides and sessions to highlight use of associated referencing tools. Sessions on a variety of topics, such as supporting study skills and reading originality reports, have been provided for students on taught, undergraduate and postgraduate programmes. This provision has included students working on collaborative partners' sites and also those on research programmes. Alongside the activities with students, "designing out" approaches have been embedded in staff development within the educator community at Northumbria. Formative use of Turnitin is integrated throughout programmes and academic practice development is formally recognised within the University Learning and Teaching Strategy's focus on information literacy. This article outlines and reviews these activities in a critical institutional context and evaluates responses from a variety of students and educators to determine how effective these measures have been
A realistic assessment of methods for extracting gene/protein interactions from free text
Background: The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger. Results: Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions. Conclusion: In terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community
Transcriptomic analysis of the entomopathogenic nematode Heterorhabditis bacteriophora TTO1
Background:
The entomopathogenic nematode Heterorhabditis bacteriophora and its symbiotic bacterium, Photorhabdus luminescens, are important biological control agents of insect pests. This nematode-bacterium-insect association represents an emerging tripartite model for research on mutualistic and parasitic symbioses. Elucidation of mechanisms underlying these biological processes may serve as a foundation for improving the biological control potential of the nematode-bacterium complex. This large-scale expressed sequence tag (EST) analysis effort enables gene discovery and development of microsatellite markers. These ESTs will also aid in the annotation of the upcoming complete genome sequence of H. bacteriophora.
Results:
A total of 31,485 high quality ESTs were generated from cDNA libraries of the adult H. bacteriophora TTO1 strain. Cluster analysis revealed the presence of 3,051 contigs and 7,835 singletons, representing 10,886 distinct EST sequences. About 72% of the distinct EST sequences had significant matches (E value < 1e-5) to proteins in GenBank's non-redundant (nr) and Wormpep190 databases. We have identified 12 ESTs corresponding to 8 genes potentially involved in RNA interference, 22 ESTs corresponding to 14 genes potentially involved in dauer-related processes, and 51 ESTs corresponding to 27 genes potentially involved in defense and stress responses. Comparison to ESTs and proteins of free-living nematodes led to the identification of 554 parasitic nematode-specific ESTs in H. bacteriophora, among which are those encoding F-box-like/WD-repeat protein theromacin, Bax inhibitor-1-like protein, and PAZ domain containing protein. Gene Ontology terms were assigned to 6,685 of the 10,886 ESTs. A total of 168 microsatellite loci were identified with primers designable for 141 loci.
Conclusion:
A total of 10,886 distinct EST sequences were identified from adult H. bacteriophora cDNA libraries. BLAST searches revealed ESTs potentially involved in parasitism, RNA interference, defense responses, stress responses, and dauer-related processes. The putative microsatellite markers identified in H. bacteriophora ESTs will enable genetic mapping and population genetic studies. These genomic resources provide the material base necessary for genome annotation, microarray development, and in-depth gene functional analysis
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