Bio-medical Ontologies Maintenance and Change Management

Classifying Patterns in Bioinformatics Databases 189
Inside DNA chains there are some nucleotide strings known as genes,which
are sequences of nucleotide, also called exons. These exons are of particular
interest, since they have a particular correspondence with a certain protein:
they code for this particular protein. However, the exons are located in DNA
chains in a disorderly fashion, making it very difficult to take a portion of the
chain and say that it codes for a certain protein, as is. Between exons there
are sequences which do not code for a protein, called introns; the sequences
that do not code for proteins and are between genes are known as intergenetic
regions [7], [10], [11].
On the other hand, proteins are polypeptides formed inside cells as sequences
of 20 different aminoacids [12], which are denoted by 20 different
letters. Each of these 20 aminoacids is coded by one or more codons [9]. The
chemical properties differentiating the 20 aminoacids make them group together
to conform proteins with certain tridimensional structures, defining
the specific functions of a cell [11].
2.1.2 Main Problems in Bioinformatics
The different problems addressed by Bioinformatics can be classified into
three categories: genomic tasks, proteomic tasks, andgene expression tasks.
The first refers to the study of various genomes when taken as entities with
similar contents, while the second refers to the study of all the proteins which
arise from a genome [13]. The last one is related to studying the relationships
between a nucleotide string and the proteins generated by that string. In this
chapter we are interested on the genomic task, particularly on both promoter
and splice-junction identification. Promoters are regions located immediately
before each gene, indicating that what follows is a gene; they also regulate
the beginning of transcription [14]. A splice-junction zone is where an intron
becomes an exon an viceversa, this is important in order to identified which
segments of the sequence code for a protein[14].
2.1.3 Computational Tools Applied to Bioinformatics
One of the most used computational methods in Bioinformatics are artificial
neural networks. These are a set of models which emulate biological neural
networks. Some of the tasks in which artificial neural networks are most
employed are classification and function approximation problems. However,
it is noteworthy to mention that, even though neural networks and associative
memories are equivalent models, as shown by Hopfiled [15], associative
memories have not been much used in Bioinformatics. Results obtained by
feed-forward back-propagation neural networks have been compared to those
shown by BLAST, one of the most widely used tools in Bioinformatics. In
these comparisons, the neural network trained with the back-propagation
algorithm presented a better processing speed, but not as effective results
[16]-[20].