TY - CHAP

T1 - Finding optimal pairs of patterns

AU - Bannai, Hideo

AU - Hyyrö, Heikki

AU - Shinohara, Ayumi

AU - Takeda, Masayuki

AU - Nakai, Kenta

AU - Miyano, Satoru

PY - 2004

Y1 - 2004

N2 - We consider the problem of finding the optimal pair of string patterns for discriminating between two sets of strings, i.e. finding the pair of patterns that is best with respect to some appropriate scoring function that gives higher scores to pattern pairs which occur more in the strings of one set, but less in the other. We present an O(N2) time algorithm for finding the optimal pair of substring patterns, where N is the total length of the strings. The algorithm looks for all possible Boolean combination of the patterns, e.g. patterns of the form p ∧ ¬ q, which indicates that the pattern pair is considered to match a given string s, if p occurs in s, AND q does NOT occur in s. The same algorithm can be applied to a variant of the problem where we are given a single set of sequences along with a numeric attribute assigned to each sequence, and the problem is to find the optimal pattern pair whose occurrence in the sequences is correlated with this numeric attribute. An efficient implementation based on suffix arrays is presented, and the algorithm is applied to several nucleotide sequence datasets of moderate size, combined with microarray gene expression data, aiming to find regulatory elements that cooperate, complement, or compete with each other in enhancing and/or silencing certain genomic functions.

AB - We consider the problem of finding the optimal pair of string patterns for discriminating between two sets of strings, i.e. finding the pair of patterns that is best with respect to some appropriate scoring function that gives higher scores to pattern pairs which occur more in the strings of one set, but less in the other. We present an O(N2) time algorithm for finding the optimal pair of substring patterns, where N is the total length of the strings. The algorithm looks for all possible Boolean combination of the patterns, e.g. patterns of the form p ∧ ¬ q, which indicates that the pattern pair is considered to match a given string s, if p occurs in s, AND q does NOT occur in s. The same algorithm can be applied to a variant of the problem where we are given a single set of sequences along with a numeric attribute assigned to each sequence, and the problem is to find the optimal pattern pair whose occurrence in the sequences is correlated with this numeric attribute. An efficient implementation based on suffix arrays is presented, and the algorithm is applied to several nucleotide sequence datasets of moderate size, combined with microarray gene expression data, aiming to find regulatory elements that cooperate, complement, or compete with each other in enhancing and/or silencing certain genomic functions.

UR - http://www.scopus.com/inward/record.url?scp=35048820942&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048820942&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-30219-3_38

DO - 10.1007/978-3-540-30219-3_38

M3 - Chapter

AN - SCOPUS:35048820942

SN - 3540230181

SN - 9783540230182

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 450

EP - 462

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Jonassen, Inge

A2 - Kim, Junhyong

PB - Springer Verlag

ER -