Data Mining Solution for Transcription as a part of Gene Expression

The biological data is available in different formats and is comparatively more complex. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of biology. In the present research work, transcription is simulated as a part of gene expression. It involves conversion DNA to pre-mRNA which is based on the concept of replacing Uracil with Thymine. Association mining is utilized to retrieve the results. The process of splicing is also implemented based on GU-AG rule which removes the introns from the input pre-mRNA sequence. The final output sequence is m-RNA. The output sequence is mRNA. Induction and Cluster Analysis technique of data mining are used to retrieve the results