Authors : A. Surendar, M. Arun and A.M. Basha
Abstract: The growing importance of medical solutions requires special hardware and special devices to perform high dimensional data processing. The medical problems like gene selection, protein sequence identification and DNA sequence detection has great impact in this area. To perform such high dimensional process, it requires special hardware implementation and designing such implementation also increases the complexity of efficiency. The FPGA (Field Programmable Gate Arrays) is the well-known design and we propose a novel algorithm for sequence detection in any of bioinformatics data. Unlike previous methods, the proposed method identifies the sequence of each factor on the basis of their occurrence. For each size of sequence, the method performs matching to find out the similarity of the sequence. Each of sequence is named as a pattern and based on the pattern being identified the method computes the similarity between each of the samples available. The method computes the multi level similarity measure with available sequences. Based on the multi level similarity measure computed a single sequence of bioinformatics can be identified. The proposed method produces efficient result in sequence detection and improves the hardware utilization and reduces the time complexity as well.
A. Surendar, M. Arun and A.M. Basha, 2016. Micro Sequence Identification of Bioinformatics Data Using Pattern Mining Techniques in FPGA Hardware Implementation. Asian Journal of Information Technology, 15: 76-81.