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|Title: ||Multiple Indexing Sequence Alignment for Group Feature Identification|
|Authors: ||WEI-YAO CHOU;TUN-WEN PAI;JIM ZONE-CHANG LAI;WEN-SHYONG TZOU;MARGARET DAH-TSYR CHANG;HAO-TENG CHANG;WEI-YI CHOU;TAN-CHI FAN|
|Contributors: ||NTOU:Department of Computer Science and Engineering|
|Issue Date: ||2011-10-21T02:34:14Z
|Publisher: ||The 3rd Annual RECOMB Satellite Workshop on Regulatory Genomics|
|Abstract: ||Abstract:A novel scheme for combinatorial patterns and exclusive group features identification employing multiple indexing sequence alignment (MISA) based on interval-jumping searching algorithms and hierarchical clustering techniques is proposed in this paper. The interval-jumping searching algorithm transforms sequences into digital number sets in order to find consensus motifs and provides approximate matching results. The searched consensus motifs with tolerant characteristics are labeled and formulated a scoring matrix in order to cluster imported sequences into several subgroups prior to the proposed multiple indexing sequence alignment. To extract distinguishable features among clustered groups, the proposed system performs various combinations of fundamental bitwise operations to obtain their distinctive characteristics. In this paper, MISA has been employed to analyze real biological data and demonstrated to be practical for searching combinatorial patterns for each subgroup and its distinctive features from other subgroups are also identified for further analysis. Comparisons with other existing algorithms are also presented in this paper to demonstrate superior performance of the proposed system.|
|Appears in Collections:||[資訊工程學系] 演講及研討會|
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