Computer Science – Databases
Scientific paper
2011-02-15
Computer Science
Databases
9 pages, 1 figures
Scientific paper
As the structural databases continue to expand, efficient methods are required to search similar structures of the query structure from the database. There are many previous works about comparing protein 3D structures and scanning the database with a query structure. However, they generally have limitations on practical use because of large computational and storage requirements. We propose two new types of queries for searching similar sub-structures on the structural database: LSPM (Local Spatial Pattern Matching) and RLSPM (Reverse LSPM). Between two types of queries, we focus on RLSPM problem, because it is more practical and general than LSPM. As a naive algorithm, we adopt geometric hashing techniques to RLSPM problem and then propose our proposed algorithm which improves the baseline algorithm to deal with large-scale data and provide an efficient matching algorithm. We employ the sub-sampling and Z-ordering to reduce the storage requirement and execution time, respectively. We conduct our experiments to show the correctness and reliability of the proposed method. Our experiment shows that the true positive rate is at least 0.8 using the reliability measure.
Hwang Seung-won
Roh Gook-Pil
Yi Byoung-Kee
No associations
LandOfFree
Efficient and scalable geometric hashing method for searching protein 3D structures does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Efficient and scalable geometric hashing method for searching protein 3D structures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient and scalable geometric hashing method for searching protein 3D structures will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-377861