Bio-medical Ontologies Maintenance and Change Management

204 I.R. Godínez et al.
ymin = � Λ l ∇βx 4l� =
then the vector ymin is negated
⎛ ⎞
0
⎜ 0 ⎟
⎜
˜ymin = ⎜ 0 ⎟
⎜ 1 ⎟
⎝ 0 ⎠
0
⎛ ⎞
1
⎜ 1 ⎟
⎜ 1 ⎟
⎜ 0 ⎟
⎝ 1 ⎠
1
finally, the OR binary operation is made between ymax and ˜ymin and the
classischosen
⎛ ⎞
0 or 0 0
0 or 0 ⎜ 0 ⎟
0 or 0 ⎜
= ⎜ 0 ⎟
1 or 1 ⎜ 1 ⎟
0 or 0 ⎝ 0 ⎠
0 or 0 0
�
0+0=0class1
�
0+1=1class2
�
0+0=0class3
Since class 2 has the maximum sum, 1, the pattern presented, x 4 , is assigned
to class 2. Indeed, x 4 corresponds to class 2, therefore we have correct classification.
4 Experimental Results
This section reports an experimental study of the ABMMC algorithm. In
particular, we chose two tasks relevant to Bioinformatics: promoters recognition
and splice-junction sequences identification in strings that represent
nucleotides. The databases were obtained from the Machine Learning Repository
of the University of California in Irvine [31].
Particularly, the promoters and splice-junction samples are taken from the
“E. coli promoter gene sequences (DNA) with associated imperfect domain
theory” database and “Primate splice-junction gene sequences (DNA) with
associated imperfect domain theory” database, respectively.
The promoter database has 106 instances divided into two classes, promoters
and non-promoters, 53 instances to each one. The sequences are formed
by 57 nucleotides and its binary codification is shown in table 2.
On the other hand, the splice-junction database has 3190 instances divided
into two relevant classes, exon-intron (EI) and intron-exon (IE). The distribution
for the classes is: 25% (767) for EI, 25% (768) for IE and the remaining