Constraint-based synchronization pioneered by(concurrent) logic and concurrent constraint programming is a powerful mechanism for elegantly synchronizing concurrent and distributed *** support a declarative model of c...
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Constraint-based synchronization pioneered by(concurrent) logic and concurrent constraint programming is a powerful mechanism for elegantly synchronizing concurrent and distributed *** support a declarative model of concurrency that avoids explicitly suspending and resuming *** paper describes(1) a model of concurrency based on precedence constraints,(2) its implementation as an extension to the Java programming language,and(3) how model-based verification methods can straightforwardly applied to programs in the resulting language.
We present Mining Zinc, a novel system for constraint-based pattern mining. It provides a declarative approach to data mining, where a user specifies a problem in terms of constraints and the system employs advanced t...
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ISBN:
(纸本)9781479931446
We present Mining Zinc, a novel system for constraint-based pattern mining. It provides a declarative approach to data mining, where a user specifies a problem in terms of constraints and the system employs advanced techniques to efficiently find solutions. declarative programming and modeling are common in artificial intelligence and in database systems, but not so much in data mining, by building on ideas from these communities, Mining Zinc advances the state-of-the-art of declarative data mining significantly. Key components of the Mining Zinc system are (1) a high-level and natural language for formalizing constraint-based item set mining problems in models, and (2) an infrastructure for executing these models, which supports both specialized mining algorithms as well as generic constraint solving systems. A use case demonstrates the generality of the language, as well as its flexibility towards adding and modifying constraints and data, and the use of different solution methods.
Background: Many computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genom...
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ISBN:
(纸本)9781450372152
Background: Many computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). Hi-C data is now being generated at increasingly high resolutions. As this resolution increases, it has become computationally infeasible to predict a 3D genome organization with the majority of existing methods. None of the existing solution methods have utilized a non-procedural programming approach (such as integer programming) despite the established advantages and successful applications of such approaches for predicting high-resolution 3D structures of other biomolecules. Our objective was to develop a new solution to the 3D-GRP that utilizes non-procedural programming to realize the same ***: In this paper, we present a three-step consensus method (called GeneRHi-C; pronounced "generic") for solving the 3D-GRP which utilizes both new and existing techniques. Briefly, (1) the dimensionality of the 3D-GRP is reduced by identifying a biologically plausible, ploidy-dependent subset of interactions from the Hi-C data. This is performed by modelling the task as an optimization problem and solving it efficiently with an implementation in a non-procedural programming language. The second step (2) generates a biological network (graph) that represents the subset of interactions identified in the previous step. Briefly, genomic bins are represented as nodes in the network with weighted-edges representing known and detected interactions. Finally, the third step (3) uses the ForceAtlas 3D network layout algorithm to calculate (x, y, z) coordinates for each genomic region in the contact map. The resultant predicted genome organization represents the interactions of a population-averaged consensus structure. The overall workflow was tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The resul
We describe the foundations of a system for rule-based programming which integrates two powerful mechanisms: (1) matching with context variables, sequence variables, and regular constraints for their matching values;a...
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We describe the foundations of a system for rule-based programming which integrates two powerful mechanisms: (1) matching with context variables, sequence variables, and regular constraints for their matching values;and (2) strategic programming with labeled rules. The system is called ρLog, and is built on top of the pattern matching and rule-based programming capabilities of Mathematica.
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