Bibliography of: Programming Languages

  1. Hofestadt, R.. "Grammatical formalization of metabolic processes." Proc Int Conf Intell Syst Mol Biol. vol. 1. 1993. pp. 181-9.
    [ PubMed ]

    In the field of biotechnology and medicine it is of interest to model and simulate metabolic processes. The usual methods to model metabolic pathways are chemical descriptions and differential equations. Moreover, the graph theoretical aspect is discussed and the development of expert systems is in process. In this paper we present the formalization of metabolic processes. Our formalization is based on the theory of formal languages. This formalization is called genetic grammar and represents an expansion of the Semi-Thue-System.

    Keywords: *Computer Simulation ; *Expert Systems ; Gene Expression ; *Metabolism ; *Models Biological ; Programming Languages


  2. Karp, P.D. and Paley, S.M.. "Integrated access to metabolic and genomic data." J Comput Biol. 3 (1). 1996. pp. 191-212.
    [ .pdf ] [ .ps ] [ PubMed ]

    The EcoCyc system consists of a knowledge base (KB) that describes the genes and intermediary metabolism of Escherichia coli, and a graphical user interface (GUI) for accessing that knowledge. This paper addresses two problems: How can we create a GUI that provides integrated access to metabolic and genomic data? We describe the design and implementation of visual presentations that closely mimic those found in the biology literature, and that offer hypertext navigation among related entities, and multiple views of the same entity. We employ a frame knowledge representation system (FRS) called HyperTHEO to manage the EcoCyc knowledge base. Among the advantages of FRSs are an expressive data model for capturing the complexities of biological information, and schema-evolution capabilities that facilitate the constant schema changes that biological databases tend to undergo. HyperTHEO also includes rule-based inference facilities that are the foundation of expert systems, a constraint language for maintaining data integrity, and a declarative query language. A graphic KB editor and browser allow the EcoCyc developers to interactively inspect and modify this evolving KB.

    Keywords: *Artificial Intelligence ; Computer Communication Networks ; Computer Graphics ; Computers ; *Database Management Systems ; Escherichia coli_*genetics ; Escherichia coli_*metabolism ; *Genome ; Bacterial ; Programming Languages ; Systems Integration ; User-Computer Interface


  3. Rana, O.F.. "Automating parallel implementation of neural learning algorithms." Int J Neural Syst. 10 (3). 2000. pp. 227-41.
    [ PubMed ]

    Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.

    Keywords: *Algorithms ; Artificial Intelligence ; Computers ; Models Neurological ; *Neural Networks (Computer) ; Programming Languages