Pattern recognition techniques in microarray data analysis: a survey.
Recent development of technologies (e.g., microarray technology) that are capable of producing massive amounts of genetic data has highlighted the need for new pattern recognition techniques that can mine and discover biologically meaningful knowledge in large data sets. Many researchers have begun an endeavor in this direction to devise such data-mining techniques. As such, there is a need for survey articles that periodically review and summarize the work that has been done in the area. This article presents one such survey. The first portion of the paper is meant to provide the basic biology (mostly for non-biologists) that is required in such a project. This part is only meant to be a starting point for those experts in the technical fields who wish to embark on this new area of bioinformatics. The second portion of the paper is a survey of various data-mining techniques that have been used in mining microarray data for biological knowledge and information (such as sequence information). This survey is not meant to be treated as complete in any form, since the area is currently one of the most active, and the body of research is very large. Furthermore, the applications of the techniques mentioned here are not meant to be taken as the most significant applications of the techniques, but simply as examples among many.
Valafar
Faramarz
F
Department of Computer Science, San Diego State University, California 92182, USA. faramarz@sciences.sdsu.edu
D000465:Algorithms; D001483:Base Sequence; D004247:DNA / Q000737:chemistry; D016678:Genome; D008390:Markov Chains; D016571:Neural Networks, Computer; D009690:Nucleic Acid Conformation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D040081:Protein Array Analysis / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000235:genetics
D011506:Proteins; D004247:DNA