XTractor Premium Application Note on Nature|Methods

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Published Literature findings act as a key driver for decision making in drug discovery and biotechnology industry. Majority of the researchers prefer PubMed for published literature since it is a free resource for dissemination of scientific information. Data mining biomedical information from PubMed has been a long-standing problem for many researchers across the globe. On an average more than 50,000 scientific abstracts get published in PubMed every month and for a researcher it is a highly time consuming task to pick the most relevant abstracts everyday and annotate them accurately for research purposes. Though many text mining NLP (Natural Language Processing) engines are used to address this problem, manually annotated data has its own advantages in terms of accuracy and quality of the data that is captured. However, one of the major disadvantages with manual data mining still happens to be the processing time and its cost intensive nature.

So the need arises for a system, which can accurately process this vast repertoire of scientific information with a quick turn around time. We have tried to address both these problems with XTractor. XTractor provides the researchers with the most relevant scientific facts manually annotated (It is proven by our analysis to be 12-35% more accurate than the text mining engines) and delivered with in the shortest turnaround time. Also, XTractor comes with advanced analytical features such as Semantic Search, Concept linking and comprehensive downloadable reports for faster analysis of the biomedical data.

Click here to view the full application note at nature|methods

XTractor Premium - A Platform for discovery, knowledge sharing, analysis and modelling of published biomedical facts.









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