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Introduction Research published in academic journals plays a crucial role in improving drug discovery by revealing new biological targets, mechanisms, and treatment strategies. However, many biomedical researchers lack the expertise to use these advanced data processing techniques.
In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Datadiscovery tools available in the market to take their brand forward.
We envisioned harnessing the power of our products to elevate our entire content publishing process, thereby facilitating in-depth knowledge exploration. We started with our marketing content and quickly expanded that to also integrate a set of workflows for data and content management.
In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and DataDiscovery tools available in the market to take their brand forward.
The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprise data? Source: tag.ontotext.com.
Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. 1998) and others).
It demonstrates how GraphDB and metaphactory work together and how you can employ the platform’s intuitive and out-of-the-box search, visualization and authoring components to empower end users to consume data from your knowledge graph. Data normalization is an essential step in the data preparation process.
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