The phrase "already categorized as" is correct and usable in written English.
It can be used when referring to something that has been classified or sorted into a specific category prior to the current discussion or context.
Example: "The documents have already been categorized as confidential, so please handle them with care."
Alternatives: "previously classified as" or "designated as".
The flow was already categorized as the largest offshore oil spill in the nation's history, but the new figures sharply increase previous estimates.
The electromagnetic radiation is already categorized as the fourth most serious source of public pollution following the noise, air and water pollution (Ozen et al. 2012).
Second, if a TFBS was detected only under one of the two background nucleotide distributions, then it was counted as a TFBS only if the same sequence was already categorized as an unambiguous TFBS for the same TF in another strain (supplementary fig. S1, Supplementary Material online).
Individuals already treated for diabetes with insulin, oral hypoglycemic agents, or a physician-prescribed diet were categorized as known diabetic patients, irrespective of their glucose levels.
The traumas would be categorized as moderate.
All adverse events were categorized as mild.
Some Democrats have already categorized the outcome as a rebuke to Trump.
Some lawmakers have already categorized today's revelation as proof of a Russian disinformation campaign.
We used again as a gold standard the genetic modifiers of HD that were already categorized manually by experts in that field.
Because of their involvement in a fundamental cellular process, all six of these genes are essential for viability, and renamed MCM2 through MCM7, the name MCM1 had already been categorized as a transcription factor and it does not belong to the MCM2 9 family.
Firstly, the bioNerDS dictionary entries have already been categorized as either databases or software wherever possible, and secondly, we score names by counting the indicative keywords found around all the name mentions within text during extraction, finally taking the majority decision (e.g. more database indicative terms than software ones).
