mining biological repetitive sequences using support vector machines and fuzzy svm

structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. biggest class of the repetitive subsequences is “transposable elements” which has its own sub-classes upon contexts’ structures. many researches have been performed to criticality determine the structure and function of repetitive subsequences. the sequencing noises and the sequences’ substitutions probability are obstacles of these researches. some statistical and approximation algorithms have introduced to tackle these obstacles. by introducing conspicuous statistical machine learning methods upon support vector machines, machine learning approaches act as potent methods to solve the pattern-finding problem. support vector machines methods are time efficient approaches, which based on their parameters can be precise and accurate. in this review, mathematical definition of structural repetitive subsequences are introduced, thereafter proposed algorithm to tackle simple pattern finding problem, which can be applicable on structural patterns are reviewed. theoretical aspects of support vector machines on computational biology platform are considered. finally, novel evolutionary fuzzy svm will be introduced, which is applicable on wide range of bioinformatics problems especially the problem of structural repetitive subsequences.