A Top-Down Approach for Mining Most Specific Frequent Patterns in Biological Sequence Data Export

@misc{citeulike:2139530, abstract = {The emergence of automated high-throughput sequencing technologies has resulted in a huge increase of the amount of DNA and protein sequences available in public databases. A promising approach for mining such biological sequence data is mining frequent subsequences. One way to limit the number of patterns discovered is to determine only the most specific frequent subsequences which subsume a large number of more general patterns. In the biological domain, a wealth of knowledge on the...}, author = {Zhang, Xiang}, citeulike-article-id = {2139530}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.6630}, keywords = {bioinformatics, computational-genetics}, posted-at = {2007-12-18 05:25:13}, priority = {2}, title = {A Top-Down Approach for Mining Most Specific Frequent Patterns in Biological Sequence Data}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.6630} }

TY - GEN ID - citeulike:2139530 L3 - citeulike-article-id:2139530 N2 - The emergence of automated high-throughput sequencing technologies has resulted in a huge increase of the amount of DNA and protein sequences available in public databases. A promising approach for mining such biological sequence data is mining frequent subsequences. One way to limit the number of patterns discovered is to determine only the most specific frequent subsequences which subsume a large number of more general patterns. In the biological domain, a wealth of knowledge on the... TI - A Top-Down Approach for Mining Most Specific Frequent Patterns in Biological Sequence Data KW - bioinformatics KW - computational-genetics AU - Zhang, Xiang UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.6630 ER -