270 research outputs found

    Integrating protein-protein interactions and text mining for protein function prediction

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    <p>Abstract</p> <p>Background</p> <p>Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature.</p> <p>Results</p> <p>Using this procedure, more than 80% of the GO annotations for proteins with highly conserved orthologs that are available in UniProtKb/Swiss-Prot could be verified automatically. For a subset of proteins we predicted new GO annotations that were not available in UniProtKb/Swiss-Prot. All predictions were correct (100% precision) according to the verifications from a trained curator.</p> <p>Conclusion</p> <p>Our method of integrating CCSs and literature mining is thus a highly reliable approach to predict GO annotations for weakly characterized proteins with orthologs.</p

    A realistic assessment of methods for extracting gene/protein interactions from free text

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    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

    Stability of sub-surface oxygen at Rh(111)

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    Using density-functional theory (DFT) we investigate the incorporation of oxygen directly below the Rh(111) surface. We show that oxygen incorporation will only commence after nearly completion of a dense O adlayer (\theta_tot = 1.0 monolayer) with O in the fcc on-surface sites. The experimentally suggested octahedral sub-surface site occupancy, inducing a site-switch of the on-surface species from fcc to hcp sites, is indeed found to be a rather low energy structure. Our results indicate that at even higher coverages oxygen incorporation is followed by oxygen agglomeration in two-dimensional sub-surface islands directly below the first metal layer. Inside these islands, the metastable hcp/octahedral (on-surface/sub-surface) site combination will undergo a barrierless displacement, introducing a stacking fault of the first metal layer with respect to the underlying substrate and leading to a stable fcc/tetrahedral site occupation. We suggest that these elementary steps, namely, oxygen incorporation, aggregation into sub-surface islands and destabilization of the metal surface may be more general and precede the formation of a surface oxide at close-packed late transition metal surfaces.Comment: 9 pages including 9 figure files. Submitted to Phys. Rev. B. Related publications can be found at http://www.fhi-berlin.mpg.de/th/paper.htm

    Sugar-Sweetened Beverages and Adverse Human Health Outcomes: An Umbrella Review of Meta-Analyses of Observational Studies

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    Our aim was to conduct an umbrella review of evidence from meta-analyses of observational studies investigating the link between sugar-sweetened beverage consumption and human health outcomes. Using predefined evidence classification criteria, we evaluated evidence from 47 meta-analyses encompassing 22,055,269 individuals. Overall, 79% of these analyses indicated direct associations between greater sugar-sweetened beverage consumption and higher risks of adverse health outcomes. Convincing evidence (class I) supported direct associations between sugar-sweetened beverage consumption and risks of depression, cardiovascular disease, nephrolithiasis, type 2 diabetes mellitus, and higher uric acid concentrations. Highly suggestive evidence (class II) supported associations with risks of nonalcoholic fatty liver disease and dental caries. Out of the remaining 40 meta-analyses, 29 were graded as suggestive or weak in the strength of evidence (classes III and IV), and 11 showed no evidence (class V). These findings inform and provide support for population-based and public health strategies aimed at reducing sugary drink consumption for improved health.</p

    Text mining for biology - the way forward: opinions from leading scientists

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    This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress

    Влияние ионной бомбардировки на формирование поверхностных слоев при азотировании в безводородной плазме газового разряда

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    В работе приведены результаты изучения процессов упрочнения стальных образцов в безводородной плазме низковольтного газового разряда в вакууме. В отличие от тлеющего разряда, широко применяемого в промышленных технологиях для проведения ионно-плазменного азотирования, в данном типе газового разряда из-за низкого давления заметно выражены эффекты распыления поверхности обрабатываемых деталей. Данное обстоятельство однозначно должно приводить к интенсификации диффузионных процессов. В работе описаны параметры оборудования, приводятся экспериментальные данные по измерениям шероховатости, микротвердости и структуре поверхностей легированных сталей. Отдельное внимание уделено вопросу азотирования закаленных сталей в данном типе разряда в диапазоне температур отпуска.In this work presents the results of studying the processes of hardening of steel samples in hydrogen-free plasma of low-voltage gas discharge in vacuum. In contrast to the glow discharge, widely used in industrial technologies for ion-plasma nitriding, in this type of gas discharge due to the low pressure, the effects of spraying the surface of the processed parts are markedly expressed. This fact should definitely lead to the intensification of diffusion processes. The paper describes the parameters of the equipment, provides experimental data on the measurements of roughness, microhardness and structure of the surfaces of alloy steels. Special attention is paid to the issue of nitriding of hardened steels in this type of discharge in the tempering temperature range
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