A comparative study on existing methodologies to predict dominating patterns amongst biological sequences

Abstract

Data Mining is the process of extracting or mining the patterns from very large amount of biological datasets. Utilization of Data mining algorithms can reveal biological relevant associations between different genes and gene expression. In Data Mining, several techniques are available for predicting frequent patterns. One among the technique is association rule mining algorithm; which can be applied for solving the crucial problems faced in the field of biological science. From the literature, various algorithms have been employed in generating frequent patterns for distinct application. These algorithms have some limitations in predicting frequent patterns, such as space, time complexity and accuracy. In order to overcome these drawbacks, the study is made on existing algorithms for generating frequent patterns from the biological sequences. The literature survey gives a significant number of methods were generated for predicting associative patterns. The proposed system has to be developed for solving problems in biological science. Biological sequence may be a collection of DNA sequence, Gene expression sequence or Protein sequence for a specific viral disease. Amino acids are the building blocks of proteins. Proteins are organic compounds made up of amino acids arranged in a linear chain and folded into a globular form. The future proposal not only leads in predicting the frequent patterns; it will also satisfy some factors such as: time complexity, space and predict accurate solution to the required problem. With the help of these three factors into consideration and efficient algorithm can be identified for predicting the dominating amino acids for any kind of specific biological implication.