Bioinformatics involves the manipulation, searching, and data mining of biological data, and this includes DNA sequence data.
The prediction capability of SVM can be further enhanced with increasing availability of biological data and more extensive knowledge about sequence, structure, transcription, post-transcriptional processing features that define the functional profiles of proteins and peptides.
To refer to a particular vocabulary describing knowledge associated with a patchwork of biological data, objects, sequences, biological functions and functionalities and other general features of biological processes.
Then we explore the power of the method using both simulation studies and real biological data [genomic sequences in the ENCODE regions (Birney et al., 2007)].
Figure 1 shows a typical biological data analysis sequence with SpaCEM.
Bioinformatics is the science of storing, analyzing, and utilizing information from biological data such as sequences, molecules, gene expressions, and pathways.
In this paper we propose to use biological data for generating sequences of random bits.
The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics.
Here we provide a short example of ITS2 secondary structure phylogeny, applied to biological data: we sampled sequences of three plant families using the ITS2-database browse feature (database accessed: June 2009): Thymelaeaceae (Malvales), Malvalceae (Malvales) and Sapindaceae (Sapindales).
Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature.
