Bio-medical Ontologies Maintenance and Change Management

Classifying Patterns in Bioinformatics Databases 205
50% (1655) corresponds to non-significant instances. The genetic sequences
are formed by 60 nucleotides and their binary codification is shown in table
2 too. Since we just want to differentiate between EI and IE sequences, the
remaining 50% of the instances are considered as non-significant.
Given that the alpha-beta heteroassociative memories use binary operations,
the patterns used in the learning and recalling phase must be binary
patterns too. So it was necessary to create a correspondence table between
the DNA sequences and binary patterns. It was evident, by the results shown
in this work and in [32], that the one-hot codification is the optimal.
This codification is very useful, especially in the following situations: where
in the DNA sequences there are some positions in which it is necessary to
indicate that such position could take more than one value. For example, for
the sequence ATCG, it is possible that the third position could be represented
either for “C” or “T”. In this case in order to create a binary sequence that
represents those values we just have to apply the binary operator “OR” with
the corresponding “C” and “T” sequences. This is possible due to the good
performance of the alpha-beta memories in the presence of altered patterns
(see table 2).
Table 2. Nucleotide Convertion
Nucleotide Code
A 1000
T 0100
C 0010
G 0001
D 1011
N 1111
S 0101
R 1001
In order to get an estimate of how well the algorithm learned both, the
concept of promoter and the EI or IE splice-junction sequence, a series of
experiments was made. Then the results were compared with some other
algorithms that work under the same conditions.
4.1 DNA Promoter Sequence Classification
There are two other works to which we could compare our results, J. Ortega
[33] and Baffes and Mooney [34], both of them made their experiments under
the following conditions: 85 instances were randomly taken to build the
training set and the remaining 21 were left in the test set. This procedure
was repeated 100 times. The table 3 is a comparative between [33], [34], and
the ABMMC, where N/D stand for Not-Determined.
It is evident that ABMMC overcome, without problems, the performance
of the other algorithms, even when learning with only 80 or 70 instances