Soft Computing in Bioinformatics: Genomic and Proteomic Applications

The Age of Bioinformatics

Bioinformatics has been described as the science of managing, mining, and interpreting information from biological sequences and structures (Li et al 2004). The emergence of the field has been largely attributed to the increasing amount of biomedical data created and collected and the availability and advancement of high-throughput experimental techniques. One recent example of this is the advancement of ‘lab-on-achip’ (see Figure 1) technology which allows experimentation to be performed more rapidly and at lower cost, whilst introducing the possibility of observing new phenomena or obtaining more detailed information from biologically active systems (Whitesides 2006). Such advances enable scientists to conduct experiments which result in large amounts of experimental data over a relatively short period of time. The need to analyse such experimental data has often necessitated a similarly highthroughput approach in order to produce rapid results, employing the use of efficient and flexible analysis methods and, in many areas, driving the need for every improving data analysis techniques. It is for this reason bioinformatics draws upon fields including, but not limited to, computer science, biology (including biochemistry), mathematics, statistics and physics.