The phrase "analyzed to create a" is correct and usable in written English.
It can be used when discussing a process where data or information is examined in order to produce or develop something.
Example: "The data was thoroughly analyzed to create a comprehensive report on market trends."
Alternatives: "examined to produce a" or "evaluated to generate a".
For each molecular, the length and distances between labeled sites are measured and analyzed to create a molecular map.
Previous emails marked spam can be analyzed to create a statistical model of what spam looks like (basically campaign fundraising emails except for Mel!'s).
Paleobotanical, palynological and lithological data from a section thought to represent a single interglacial/glacial phase are integrated and analyzed to create a complex picture of an evolving landscape.
Moreover, face-to-face surveys were conducted and analyzed to create a typology of forest owners based on their estimated likelihood to engage in forest land changes and elicit corresponding preferences for PSB-like forest conservation program participation.
Observations were recorded in field notes, which were later transcribed and analyzed to create a list of workflow tasks.
RNA for each condition (EtOH, DHT, and BPA) was isolated from three independent experimental cultures to determine the change in transcript levels for each condition and when evaluated in triplicate, genes passing the ANOVA criteria were further analyzed to create a cluster heat map.
That data can be parsed and analyzed to create an almost Platonic ideal of any business in any industry.
The numerical simulations are performed using the boundary conditions found from laboratory tests and the obtained results are analyzed to create and validate a feed-forward neural network model, which is implemented as a surrogate model.
Data from 1274 NSCLC patients were analyzed to create and validate a scoring system.
To allow the tagger to benefit from non-local information, we apply a two-stage tagging strategy inspired by Krishnan and Manning (2006) where a standard model is first applied to create a first-stage tagging that is then analyzed to create additional document-level features for a separately trained second-stage model that produces the final predictions.
