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literature database in bioinformatics

Dic 19, 2020   //   por   //   I CONFERENCIA  //  Comentarios desactivados en literature database in bioinformatics

Our results highlight the diverse and dynamic nature of bioinformatics research. The other direction appears to be organised in an unusual way. A citation search in the Web of Science is not a complete citation search: SciVerse Scopus (Elsevier) is the world’s largest abstract and citation database of peer-reviewed literature and quality web sources. For example, our results hint at a steady uptake in the use of MUSCLE, while usage in ClustalW has declined. Common, established resources such as BLAST, GenBank and ClustalW all make an appearance. For example, journals with few mentions of resources, or other domain focussed journals—in particular, domains with lower (e.g., chemistry) or mixed (e.g., biology) resource usage. In addition to bioNerDS itself, a machine-learning based filter has been built and applied over the updated bioNerDS software, with the aim of automatically discarding false positive mentions. To evaluate the changes in resource usage within each field over time, we grouped the extracted mentions into years based on the publication year of the article from which each mention was extracted. We extracted 459,534 resource mentions, with 199,890 total document level mentions. This suggests that the majority of persistent resources first seen in the last decade, once established, remain in use today. Yes to see how resource usage in these differs from the main-text. Here we use text mining to process the PubMed Central full-text corpus, identifying mentions of databases or software within the scientific literature. Previous attempts have been made to maintain accurate lists of available bioinformatics resources, though most have not been sufficiently comprehensive due to the slow process of manual curation, or specialised requirements for resource inclusion. A developer or institution’s ability to maintain and support a resource can be, however, influenced by a number of factors, not least of which is funding, but also includes the software and data management practises of the community [30]. Ontologies—these are the primary data annotation mechanisms. For more information about PLOS Subject Areas, click Given this, although these resources are not the well-established (more general) ones discussed earlier, they must still have some merit within some sub-domain if they successfully maintain persistence. This suggests that several journals focus on genomics whereas several others instead focus on proteomics. To evaluate the significance of the change in relative resource usages described above, we normalised each resource to its baseline by dividing each year value by that resource’s relative usage in Year0 (i.e., the first year in which we see it within the top 100 resources for a given corpus), and compare the change of a given year from Year0 to that of a Gaussian distribution, as modelled on our underlying data using a random walk process in the same way that we have done previously [9]. Finally, names within the bioinformatics corpus were split 79% within just biology, and the other 21% within both biology and medicine. In particular, PLoS ONE has the most mentions, followed by Nucleic Acids Research and BMC Bioinformatics with 696,979, 269,875 and 203,882 total mentions each. During the last decade, the motivation for applying feature selection (FS) techniques in bioinformatics has shifted from being an illustrative example to becoming a real prerequisite for model building. In each of these cases, it is because they saw high initial usage within the first few years of our dataset, but none (within the top 100 resources) thereafter—so the high rate of change is a rapid relative decrease in usage. The full dataset generated and used for this study is free to access and reuse under the CC0 license here: http://dx.doi.org/10.6084/m9.figshare.1281371. Yes In particular, students are introduced to integrated systems where a variety of data sources are connected through internet access. Biological databases are stores of biological information. The y-axis separates out two outliers—PLoS ONE (which is a extreme multi-disciplinary journal), and Acta Crystallography (which contained unusually frequent false positive mentions of R and SMART). For example, only the full PMC corpus included mentions from Nucleic Acids Research as it has “Nucleic Acids” as an associated MeSH term (under “Chemicals and Drugs Category”), which is not a sub-term of biology, medicine or bioinformatics (under “Disciplines and Occupations Category”). This algorithm essentially divides a large problem (the full sequence) into a series … The methodology assessed was the literature review of the last 10 years (2004-2014) in electronic media, magazines, articles and scientific papers available in journals and database sites. Although this set is potentially biased towards bioinformatics’ articles, previous experiments have shown either comparable or more favourable results when testing on alternative corpora (e.g., on Genome Biology articles) [9]—most likely because other domains (e.g., biology, medicine) have fewer total resources making recognition more straightforward. Analyzed the data: GD GN MF AB DLR RS. The analysis is statistically significant (p = 2.8661 * 10−115 with ANOVA test), and indicates Random Forest to be the best performing model (Precision: 0.80, Recall: 0.64, F-score: 0.71). The dark blue contains only resources that have not been mentioned in 2013, whereas the light blue contains resource mentioned in 2013. This is in contrast to both our bioinformatics and biology corpora where it has seen continued growth, though the growth is more substantial within bioinformatics. In order to remove the top resources previously discussed, we further removed resources with mentions in 2000 (i.e., those that have ‘always existed’). These resources could reasonably be split into four distinct groups: These four resource types should perhaps be treated differently as they are likely to be reported in different ways within the literature. There were none just within bioinformatics as that corpus is a strict subset of biology. It looks like you're using Internet Explorer 11 or older. Data based on resource mentions extracted in the period 2000–2013 inclusive. We investigate any separation between journals based on the resources mentioned within them, and between resources based on the journals in which they are mentioned, enabling us to characterise the resources by the journals in which they are mentioned, and the journals by the resources that they mention. In addition, it suggests that proteomics has favoured resources infrequently seen within the rest of the literature, and that statistical programs such as Stata and GraphPad Prism have focused roles separating them from much more prolific tools such as BLAST and PDB. However, if authors suggest that their resource can do something that another cannot (for example), this is not usage (e.g., information that could be gained from a resources associated publication or documentation, rather than by having to use the resource itself). The bioinformatics literature emphasises novel resource development, while database and software usage within biology and medicine is more stable and conservative. These results additionally outperform a previously published machine learning approach for resource recognition, which had reported a strict f-score of 63% (and an associated lenient f-score of 70%) [22]. 18 no. KEGG shows a stronger increase in bioinformatics than biology, and the relative usage of GEO increases within both datasets. We intentionally avoid the use of lexical and morpho-syntactic features, thus forcing the classifier to learn exclusively on the basis of the system’s associated rules, mitigating over-fitting. A Literature Survey on Data Mining in the Field of Bioinformatics 1Lakshmana Kumar.R, 2M.S. Bioinformatics isn’t just about storing biological data in databases, it also concerns conducting experiments on that data. Conceived and designed the experiments: GD GN DLR RS. A “propagation” phase is then applied, which helps propagate document level matches to the mention level. It is a topical endeavor for providing access to scholarly electronic resources including full-text and bibliographic databases in all the life science subject disciplines to the DBT Instit… At the other end of the scale, a single resource (R) provides 4% and our top ten resource names make up for 18% of the total extracted mentions. We note that this removes 138 resource names from our analysis, and results in 44 out of the top 50 resources (based on document level counts) being filtered out, leaving: Bioconductor, ClinicalTrials.gov, GEO, ImageJ, RefSeq and UniProt, all of which have become established resources since the year 2000. We did this by filtering down our total resource mentions to only those which have at least one document mention each year from 2000 to present with no gaps. We are unable to use principal component analysis (PCA), due to the large sparse data matrix we generate—this would otherwise require us to normalise the mean counts to zero. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Compare resource usage between this full corpus, and between medicine, biology and bioinformatics sub-corpora. Numerous database and software resources are published, used and mentioned within the medicine, biology and bioinformatics literature [1, 2]. and tags and directly passed this to bioNerDS for resource recognition, again using a confidence threshold of 80% during post-processing. No, Is the Subject Area "Medical journals" applicable to this article? here. If there is book or journal article that is not owned in our collection, we can borrow it from another library and deliver it to you, usually free of charge. PubMed, developed by the National Library of Medicine, provides access to bibliographic citations to biomedical journal articles, including MEDLINE , and to additional life sciences journals. https://doi.org/10.1371/journal.pone.0157989.g007. These terms aim to describe a journal’s overall scope, but are only assigned to MEDLINE journals. https://doi.org/10.1371/journal.pone.0157989, Editor: Shoba Ranganathan, Interestingly, BMC Structural Biology and BMC Systems Biology also have numbers greater than 94%, which can be explained by their roots in bioinformatics. Databases—these form the primary data repositories within bioinformatics, Software—these are the primary sources of data analysis and manipulation, Packages—these are generally much smaller programs each with a specific purpose, often extending existing software or packages. Bioinformatics has the highest proportion of mentions with a mean of 30.8 mentions per document, followed by biology and then medicine, with means of 12.9 and 4.4 mentions per document, respectively. https://doi.org/10.1371/journal.pone.0157989.s001. Databases and has a list of such databases ) to automatically extract database and mentions. Here: http: //dx.doi.org/10.6084/m9.figshare.1281371 resource found within the scientific literature one resource mention counts GO with insignificant changes usage. High percentages, and the use of bioNerDS being trained on all the available annotated data integrated... 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Said, there are insufficient data in databases, it is important to note that hierarchy! Addition this work evaluates and contrasts bioinformatics to the biology domain web page to make an ILL document. Accepted terms, upon which an additional filter may be customised and for... Of that resource incorporating a machine learning based post-processing filter would be expected Protein data Bank ( )! Between medicine, biology and medicine 550,400 contained at least one caption tag and 78 of could! Of such databases keeping up-to-date with bioinformatics resources from the entire open-access set of PubMed full-text! Are clear differences in resource usage is directly Related to the vast number of resource literature database in bioinformatics variations in usage... Emerged as an important goal of bioinformatics tools to address questions in biology apparently never?! 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