9605383
On the Application of Formal Principles to Life Science Data: a Case Study in the Gene Ontology
Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration.
16277175
Oncology Ontology in the NCI Thesaurus
The National Cancer Institute's Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT's treatment of the kinds of entities which are fundamental to an ontology of colon carcinoma. We here describe the problems we uncovered concerning classification, synonymy, relations and definitions, and we draw conclusions for the work needed to establish the NCIT as a reference ontology for the cancer domain in the future.
28378631
The Universal Medical Language System and the Gene Ontology: Some Critical Reflections
The Unified Medical Language System and the Gene Ontology are among the most widely used terminology resources in the biomedical domain. However, when we evaluate them in the light of simple principles for well-constructed ontologies we find a number of characteristic inadequacies. Employing the theory of granular partitions, a new approach to the understanding of ontologies and of the relationships ontologies bear to instances in reality, we provide an application of this theory in relation to an example drawn from the context of the pathophysiology of hypertension. This exercise is designed to demonstrate how, by taking ontological principles into account we can create more realistic biomedical ontologies which will also bring advantages in terms of efficiency and robustness of associated software applications.
14165673
Ontology for Task-Based Clinical Guidelines and the Theory of Granular Partitions
The theory of granular partitions (TGP) is a new approach to the understanding of ontologies and other classificatory systems. The paper explores the use of this new theory in the treatment of task-based clinical guidelines as a means for better understanding the relations between different clinical tasks, both within the framework of a single guideline and between related guidelines. We used as our starting point a DAML+OIL-based ontology for the WHO guideline for hypertension management, comparing this with related guidelines and attempting to show that TGP provides a flexible and highly expressive basis for the manipulation of ontologies of a sort which might be useful in providing more adequate Computer Interpretable Guideline Models (CIGMs) in the future.
17013178
Smart supply web: an application of web-based data and workflow mediation
This paper describes our vision of a Supply Web as the next major advance in the e-commerce world in terms of how e-services are delivered. After highlighting shortcomings of existing supply chain solutions, we give an architecture to show how three important technologies, namely, workow systems, data integration and on-line decision support, will have to come together to enable seamless deployment of e-service supply web solutions. We rst discuss research eorts (namely, XRL, YAT and DecisionFlows) that support the three technologies separately, and then describe the challenges of integrating them eectively.
10669659
Study of Canada/US Dollar Exchange Rate Movements Using Recurrent Neural Network Model of FX-Market
Understanding exchange rate movements has long been an extremely challenging and important task. Unsatisfactory results produced by time series regression models have led to the claim by several authors that in foreign exchange markets, past movements of the price of a given currency have no predictive power in forecasting future movements of the currency price. In this paper, we build a recurrent neural network model for FX-market to explain exchange rate movements. Asset prices are discovered in the marketplace by the interaction of market design and agents’ behaviour. The interaction is simulated by integrating 1) the FX-market mechanism; 2) an economic framework; and 3) the embedding of both tasks in neural network architectures. The results indicate that both macroeconomic and microeconomic variables are useful to forecast exchange rate changes. Results from regression model based on neural-fuzzy forecasting system are also included for comparison.
14831931
TRIPLES: a database of gene function in Saccharomyces cerevisiae
Using a novel multipurpose mini-transposon, we have generated a collection of defined mutant alleles for the analysis of disruption phenotypes, protein localization, and gene expression in Saccharomyces cerevisiae. To catalog this unique data set, we have developed TRIPLES, a Web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces. Encompassing over 250 000 data points, TRIPLES provides convenient access to information from nearly 7800 transposon-mutagenized yeast strains; within TRIPLES, complete data reports of each strain may be viewed in table format, or if desired, downloaded as tab-delimited text files. Each report contains external links to corresponding entries within the Saccharomyces Genome Database and International Nucleic Acid Sequence Data Library (GenBank). Unlike other yeast databases, TRIPLES also provides on-line order forms linked to each clone report; users may immediately request any desired strain free-of-charge by submitting a completed form. In addition to presenting a wealth of information for over 2300 open reading frames, TRIPLES constitutes an important medium for the distribution of useful reagents throughout the yeast scientific community. Maintained by the Yale Genome Analysis Center, TRIPLES may be accessed at http://ycmi.med.yale.edu/ygac/triples.htm
14590041
Designing Access Methods for Bitemporal Databases
By supporting the valid and transaction time dimensions, bitemporal databases represent reality more accurately than conventional databases. The authors examine the issues involved in designing efficient access methods for bitemporal databases, and propose the partial-persistence and the double-tree methodologies. The partial-persistence methodology reduces bitemporal queries to partial persistence problems for which an efficient access method is then designed. The double-tree methodology "sees" each bitemporal data object as consisting of two intervals (a valid-time and a transaction-time interval) and divides objects into two categories according to whether the right endpoint of the transaction time interval is already known. A common characteristic of both methodologies is that they take into account the properties of each time dimension. Their performance is compared with a straightforward approach that "sees" the intervals associated with a bitemporal object as composing one rectangle, which is stored in a single multidimensional access method. Given that some limited additional space is available, the experimental results show that the partial-persistence methodology provides the best overall performance, especially for transaction timeslice queries. For those applications that require ready, off-the-shelf, access methods, the double-tree methodology is a good alternative.
1630993
Join-distinct aggregate estimation over update streams
There is growing interest in algorithms for processing andquerying continuous data streams (i.e., data that is seenonly once in a fixed order) with limited memory resources.Providing (perhaps approximate) answers to queries over suchstreams is a crucial requirement for many application environments;examples include large IP network installations where performancedata from different parts of the network needs to be continuouslycollected and analyzed.
The ability to estimate the number of distinct (sub)tuples inthe result of a join operation correlating two data streams (i.e.,the cardinality of a projection with duplicate elimination over ajoin) is an important requirement for several data-analysisscenarios. For instance, to enable real-time traffic analysis andload balancing, a network-monitoring application may need toestimate the number of distinct (<i>source</i>,destination) IP-address pairs occurring in the stream of IP packetsobserved by router <i>R</i><inf>1</inf>,where the source address is also seen in packets routed through adifferent router <i>R</i><inf>2</inf>.Earlier work has presented solutions to the individual problems ofdistinct counting and join-size estimation (without duplicateelimination) over streams. These solutions, however, arefundamentally different and extending or combining them to handleour more complex "Join-Distinct" estimation problem is far fromobvious. In this paper, we propose the <i>first</i>space-efficient algorithmic solution to the general Join-Distinctestimation problem over continuous data streams (our techniques canactually handle general <i>update streams</i>comprising tuple deletions as well as insertions). Our estimatorsare probabilistic in nature and rely on novel algorithms forbuilding and combining a new class of hash-based synopses (termed"JD <i>sketches</i>") for individual update streams. Wedemonstrate that our algorithms can provide low error,high-confidence Join-Distinct estimates using only small space andsmall processing time per update. In fact, we present lower boundsshowing that the space usage of our estimators is within smallfactors of the best possible for the Join-Distinct problem.Preliminary experimental results verify the effectiveness of ourapproach.