Baker, P.G., Brass, A., Bechhofer, S., Goble, C.A., Paton, N.W., and Stevens, R.. "TAMBIS--Transparent Access to Multiple Bioinformatics Information Sources." Proc Int Conf Intell Syst Mol Biol.
vol. 6.
1998.
pp. 25-34.
[ .pdf ] [ .ps ] [ PubMed ] [ WebSite ]
The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.
Keywords: Artificial Intelligence ; *Computational Biology ; Databases Factual ; User-Computer Interface
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
Karp, P.D.. "Pathway databases: a case study in computational symbolic theories." Science. 293
(5537).
2001.
pp. 2040-4.
[ .pdf ] [ PubMed ]
A pathway database (DB) is a DB that describes biochemical pathways, reactions, and enzymes. The EcoCyc pathway DB (see http://ecocyc.org) describes the metabolic, transport, and genetic-regulatory networks of Escherichia coli. EcoCyc is an example of a computational symbolic theory, which is a DB that structures a scientific theory within a formal ontology so that it is available for computational analysis. It is argued that by encoding scientific theories in symbolic form, we open new realms of analysis and understanding for theories that would otherwise be too large and complex for scientists to reason with effectively.
Keywords: Artificial Intelligence ; *Computational Biology ; Culture Media ; *Databases Factual ; Escherichia coli_enzymology ; Escherichia coli_*genetics ; Escherichia coli_growth and development ; Escherichia coli_*metabolism ; *Genome Bacterial ; Internet ; Software
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