G
Gene Myers
Researcher at University of Arizona
Publications - 20
Citations - 4030
Gene Myers is an academic researcher from University of Arizona. The author has contributed to research in topics: Approximate string matching & Pattern matching. The author has an hindex of 14, co-authored 18 publications receiving 3804 citations.
Papers
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Journal ArticleDOI
Suffix arrays: a new method for on-line string searches
Udi Manber,Gene Myers +1 more
TL;DR: A new and conceptually simple data structure, called a suffixarray, for on-line string searches is introduced in this paper, and it is believed that suffixarrays will prove to be better in practice than suffixtrees for many applications.
BookDOI
Algorithms in Bioinformatics
Rita Casadio,Gene Myers +1 more
TL;DR: A Lookahead Branch-and-Bound Algorithm for the Maximum Quartet Consistency Problem and Semi-definite Programming to Enhance Supertree Resolvability are presented.
Journal ArticleDOI
A fast bit-vector algorithm for approximate string matching based on dynamic programming
TL;DR: An algorithm of comparable simplicity that requires only O(kn/w) time by virtue of computing a bit representation of the relocatable dynamic programming matrix for the approximate string matching problem, and is found to be more efficient than the previous results for many choices of k and small.
Proceedings ArticleDOI
Suffix arrays: a new method for on-line string searches
Udi Manber,Gene Myers +1 more
TL;DR: In this paper, a new data structure, called a suffixarray, is introduced for on-line string searches, which can be constructed in O(N) expected time. But, in practice, suffix arrays use three to five times less space than suffixtrees.
Book ChapterDOI
Efficient Local Alignment Discovery amongst Noisy Long Reads
TL;DR: This paper presents a very efficient yet highly sensitive, threaded filter, based on a novel sort and merge paradigm, that proposes seed points between pairs of reads that are likely to have a significant local alignment passing through them and presents a linear expected-time heuristic based on the classic O(nd) difference algorithm.