Abstract
Our previous article presented several computational models inspired on biological models, such as neural networks, evolutionary computation, swarm intelligence, and the artificial immune system. It also explained the most common problems in bioinformatics to which these models can be applied. The present article presents a series of approaches to bioinformatics tasks that were developed by means of artificial intelligence techniques and focus on bioinspired algorithms such as artificial neural networks and evolutionary computation.
TopBackground
Previous publications have focused on the use of bio-inspired and other artificial intelligence techniques. Keedwell (2005) has summarized the foundations of molecular biology, the main problems in bioinformatics, and the existing solutions based on artificial intelligence. Baldi (Baldi, 2001) also describes various techniques for problem-solving in bioinformatics. Other generalizing works on this subject can be found in (Larrañaga, 2006), whereas more specialized works focus on solutions based on evolutionary computation (Pal, 2006) or artificial life (Das, 2007).
Bio-Inspired Techniques
The following section describes how the techniques that were mentioned in our article Bio-inspired Algorithms in Bioinformatics I have been used to solve the main problems in bioinformatics.
Key Terms in this Chapter
Bioinformatics: The use of applied mathematics, informatics, statistics, and computer science to study biological systems.
Gene Mapping: Any method used for determining the location of a relative distance between genes on a chromosome.
Gene Regulatory Network: Genes that regulate or circumscribe the activity of other genes; specifically, genes with a code for proteins (repressors or activators) that regulate the genetic transcription of the structural genes and/or regulatory genes.
Sequence Alignment: The result of comparing two or more gene or protein sequences in order to determine their degree of base or amino acid similarity. Sequence alignments are used to determine the similarity, homology, function, or other degrees of relatedness between two or more genes or gene products.
Structure Prediction: Algorithms that predict the 2d or 3D structure of proteins or DNA molecules from their sequences.
Phylogeny: The evolutionary relationships among organisms. The patterns of lineage branching produced by the true evolutionary history of the organism that is being considered.
Gene Expression: The conversion of information from gene to protein via transcription and translation.