Please refer to Figure 4 for the flowchart of how genes are categorized into set A and set B. Figure 3 shows the work flow of assessing the HapMap SNPs transferability.
In addition, some genes were initially categorized into set A, but later they were included in set B. These genes do not have all their SNPs available in the NIEHS; however, a subset of them can capture all other SNPs with r2 ≥ 0.80.
In this approach software configurations are categorized into two sets 1) A set, denoted by C, of configurations which perform critical tasks (checking for uniqueness, size of index table, search operation, and collisions are treated as characteristics) 2) A set, denoted by UC, of configurations which perform non-critical tasks (the padding of special symbol in fixed-length records).
Then the genes are categorized into the set A and set B as explained above.
Seven factors categorized into two sets are scrutinized: Part I investigates the effects of four demographic variables, while Part II explores the effects of three academic (educational -related variableducational -related
The contributions of our research can be categorized into two sets.
Basically, these structures can be categorized into two sets: fixed partition and flexible partition.
In the GoM analysis, the data was categorized into six sets representing risk for GBC with respect to the investigated polymorphisms.
In order to establish the computer-based recognition database to identify good/bad colonies, hESC colonies were first manually categorized into good/bad sets.
Members of these gene families have been categorized into two sets based on their occurrences in different Prevotella genomes under study: (i) the "core" genes that exist in all 28 members of PDGHM and (ii) the "dispensable" or "flexible" genes that exist in some, but not in all Prevotella genomes under study.
We suppose a learning database of gestures to be available, categorized into a set of gesture classes (see Section 5.1 for a description of the data set considered in our work).
