As a benchmark, we have used representative subsections of the LIPIDMAPS database [27] of lipids and their structures of sizes 10, 100, and 10000 molecules in partitions DB10, DB100, and DB10000, respectively.
Notably, a complete structure of size n is generated by recursively unranking the distinct structural components from the corresponding subclasses (of substructures with sizes less than n).
We present a new data structure of size 3M bits, where M is the size of the universe at hand, for realizing a discrete priority queue.
Obviously, using uniform unranking procedures to construct the ith structure of size n for a randomly drawn number i, any structure of size n is equiprobably generated.
Third, the genetic structure of size, age and correlated traits can constrain response to selection.
A random RNA secondary structure of size n can easily be computed by drawing a random number i ∈ { 0, …, size (L, n ) - 1 } and then unranking the ith structure of size n.
Finding the connected components requires us to maintain a union find structure of size O(n k *).
In order to obtain an appropriate random sample, we have generated exactly one random structure of size n for each native RNA structure of size n given in our database, such that for each occurring size n, the random sample and the native sample contain the same number of structures having this size.
Given integer masses a1 ≤ … ≤ a n, a data structure of size na1, referred to as Extended Residue Table (ER table), is computed in a preprocessing step.
