The phrase "a given article in" is correct and usable in written English.
It can be used when referring to a specific article within a larger context, such as a publication or collection of works.
Example: "In a given article in the journal, the author discusses the implications of climate change on agriculture."
Alternatives: "an article within" or "a specific article in".
Although any given article in the PMC dataset can be compared to 72,010 other articles, the average number of article pairs with at least one shared author is 5.87.
In addition, in order to measure the general space devoted to health science articles within daily newspapers and weekly magazines, the size of a given article (ranging from 0 in the case that no article was detected up to more than one page) was divided by the total number of pages in the newspaper publishing the article.
The rate of data addition usually depends upon the quality and quantity of information in a given article, average rate of data addition varying from few articles per day to one article in a few days, preceded always by a thorough manual study of papers.
In turn, the publisher can provide their readers with access to comprehensive information available in PubChem about the chemicals mentioned in a given article.
Next, we computed click-words of a given article: popular words that appeared in at least 10% of the user queries that produced clicks for that article during the same period of time.
More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task).
Thus, literature support for prediction #1 and #2 should be considered false positives resulting from the cross-product of all disease terms with a 'chemically induced' qualifier and drugs with adverse effects in a given article.
Moreover, our click-word model is significantly more accurate at recognizing the user interest in a given article when compared with the TF IDF weighting model, evident from every evaluation measure.
Curators were interested in using text mining tools that could produce confidence scores, linkage to evidence passages in the text and ranking of automatically generated results, together with visualization aids, such as a customizable color-coding scheme for highlighting different levels annotations contained in a given article under curation.
