Super-pattern matching

Some recognition problems are either too complex or too ambiguous to be expressed as a simple pattern matching problem using a sequence or regular expression pattern. In these cases, a richer environment is needed to describe the “patterns” and recognition techniques used to perform the recognition. Some researchers have turned to artificial-intelligence techniques and multistep matching approaches for the problems of gene recognition [5], [7], [18], protein structure recognition [13], and on-line character recognition [6]. This paper presents a class of problems which involve finding matches to “patterns of patterns,” orsuper- patterns, given solutions to the lower-level patterns. The expressiveness of this problem class rivals that of traditional artificial-intelligence characterizations, and yet polynomial-time algorithms are described for each problem in the class.

[1]  Jeffrey D. Ullman,et al.  A Simple Algorithm for Global Data Flow Analysis Problems , 1975, SIAM J. Comput..

[2]  Temple F. Smith,et al.  Prediction of gene structure. , 1992, Journal of molecular biology.

[3]  Joel I. Seiferas,et al.  Correcting Counter-Automaton-Recognizable Languages , 1978, SIAM J. Comput..

[4]  Tadao Kasami,et al.  An Efficient Recognition and Syntax-Analysis Algorithm for Context-Free Languages , 1965 .

[5]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.

[6]  E. Myers,et al.  Approximate matching of regular expressions , 1989 .

[7]  Richard H. Lathrop,et al.  ARIADNE: pattern-directed inference and hierarchical abstraction in protein structure recognition , 1987, CACM.

[8]  Douglas L. Brutlag,et al.  Rapid searches for complex patterns in biological molecules , 1984, Nucleic Acids Res..

[9]  Catherine G. Wolf,et al.  On-Line Run-on Character recognizer: Design and Performance , 1991, Int. J. Pattern Recognit. Artif. Intell..

[10]  Chris A. Fields,et al.  gm: a practical tool for automating DNA sequence analysis , 1990, Comput. Appl. Biosci..

[11]  Janusz A. Brzozowski,et al.  Derivatives of Regular Expressions , 1964, JACM.

[12]  Jay Earley,et al.  An efficient context-free parsing algorithm , 1970, Commun. ACM.

[13]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[14]  A. Lapedes,et al.  Application of neural networks and other machine learning algorithms to DNA sequence analysis , 1988 .

[15]  Daniel H. Younger,et al.  Recognition and Parsing of Context-Free Languages in Time n^3 , 1967, Inf. Control..

[16]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[17]  守屋 悦朗,et al.  J.E.Hopcroft, J.D. Ullman 著, "Introduction to Automata Theory, Languages, and Computation", Addison-Wesley, A5変形版, X+418, \6,670, 1979 , 1980 .

[18]  David B. Searls.  Investigating the Linguistics of DNA with Definite Clause Grammars , 1989, NACLP.

[19]  Frances E. Allen,et al.  Control-flow analysis , 2022 .

[20]  D Sankoff,et al.  Matching sequences under deletion-insertion constraints. , 1972, Proceedings of the National Academy of Sciences of the United States of America.

[21]  G. Stormo.  Computer methods for analyzing sequence recognition of nucleic acids. , 1988, Annual Review of Biophysics and Biophysical Chemistry.