Solving Large Combinatorial Problems in Molecular Biology Using the ElipSys Parallel Constraint Logic Programming System | Zendy

Dominic A. Clark | Zendy

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Solving Large Combinatorial Problems in Molecular Biology Using the ElipSys Parallel Constraint Logic Programming System

Author(s) -

Dominic A. Clark

Publication year - 1993

Publication title -

the computer journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.319

H-Index - 64

eISSN - 1460-2067

pISSN - 0010-4620

DOI - 10.1093/comjnl/36.8.690

Subject(s) - computer science , constraint programming , pruning , theoretical computer science , constraint logic programming , prolog , logic programming , range (aeronautics) , a priori and a posteriori , constraint (computer aided design) , tree (set theory) , representation (politics) , set (abstract data type) , data structure , constraint satisfaction , artificial intelligence , mathematical optimization , programming language , mathematics , materials science , composite material , biology , geometry , philosophy , law , mathematical analysis , epistemology , political science , stochastic programming , agronomy , politics , probabilistic logic

Many areas of scientific endeavour can be characterized as the attempt to provide a consistent interpretation of a broad range of heterogeneous data and theories. In the area of protein structure prediction, for example, there are many types of diverse mutually constraining data and theories of protein structural organization that need to be integrated in order to produce a single consistent prediction (or set of predictions) of the protein structure from the experimentally derived ammo acid sequence data. Understanding the role and function of proteins in the control of cell growth is an important part of contemporary cancer research

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