Database Integration

Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classi®cations that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

A database integrating 90 years of empirical studies reporting intercorrelations among rated job performance dimensions was used to test the hypothesis of a general factor in job performance. After controlling for halo error and 3 other sources of measurement error, there remained a general factor in job performance ratings at the construct level accounting for 60% of total variance. Construct-level correlations among rated dimensions of job performance were substantially inflated by halo for both supervisory (33%) and peer (63%) intrarater correlations These findings have important implications for the measurement of job performance and for theories of job performance.

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates the recently completed P. falciparum genome sequence and annotation, as well as draft sequence and annotation emerging from other Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for intra-and inter-species comparisons. Sequence information is integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects and proteomics studies. The relational schema used to build PlasmoDB, GUS (Genomics Unified Schema) employs a highly structured format to accommodate the diverse data types generated by sequence and expression projects.

The current data explosion is intractable without advanced data management systems. The numerous data sets become really useful when they are interconnected under a uniform interface-representing the domain knowledge. The SRS has become an integration system for both data retrieval and applications for data analysis. It provides capabilities to search multiple databases by shared attributes and to query across databases fast and efficiently. Results: Here we present recent developments at the EBI SRS server (http://srs.ebi.ac.uk). The EBI SRS server contains today more than 130 biological databases and integrates more than 10 applications. It is a central resource for molecular biology data as well as a reference server for the latest developments in data integration. One of the latest additions to the EBI SRS server is the InterPro database-Integrated Resource of Protein Domains and Functional Sites. Distributed in XML format it became a turning point in low level XML-SRS integration. We present InterProScan as an example of data analysis applications, describe some advanced features of SRS6, and introduce the SRSQuickSearch JavaScript interfaces to SRS.

The rapid expansion of biomedical knowledge, reduction in computing costs, and spread of internet access have created an ocean of electronic data. The decentralized nature of our scientific community and healthcare system, however, has resulted in a patchwork of diverse, or heterogeneous, database implementations, making access to and aggregation of data across databases very difficult. The database heterogeneity problem applies equally to clinical data describing individual patients and biological data characterizing our genome. Specifically, databases are highly heterogeneous with respect to the data models they employ, the data schemas they specify, the query languages they support, and the terminologies they recognize. Heterogeneous database systems attempt to unify disparate databases by providing uniform conceptual schemas that resolve representational heterogeneities, and by providing querying capabilities that aggregate and integrate distributed data. Research in this area has applied a variety of database and knowledge-based techniques, including semantic data modeling, ontology definition, query translation, query optimization, and terminology mapping. Existing systems have addressed heterogeneous database integration in the realms of molecular biology, hospital information systems, and application portability.

This paper introduces a new model, based on so-called object-composition filters, that uniformly integrates database-like features into an object-oriented language. The focus is on providing persistent dynamic data structures, data sharing, transactions, multiple views and associative access, integrated with the object-oriented paradigm. The main contribution is that the database-like features are part of this new object-oriented model, and therefore, are uniformly integrated with object-oriented features such as data abstraction, encapsulation, message passing and inheritance. This approach eliminates the problems associated with existing systems such as lack of reusability and extensibility for database operations, the violation of encapsulation, the need to define specific types such as sets, and the incapability to support multiple views. The model is illustrated through the object-oriented language Sina.

Motivation: Assembling the relevant information needed to interpret the output from high throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labourintensive and requires considerable informatics and biological expertise. Results: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function. Availability: ONDEX is freely available under the GPL License and can be downloaded from SourceForge http://ondex.sourceforge.net/. Comment to the anonymous referees: the ONDEX frontend application, the integrated data for the application case and a tutorial that guides through the application case are included as supplementary material. The integrated data also includes subsets of commercial databases and may not be used for other purposes than reviewing this publication. The ONDEX front-end OVTK may be used and redistributed to not-for-profit organisations only.

The Protein Information Resource (PIR; http://wwwnbrf.georgetown.edu/pir/ ) supports research on molecular evolution, functional genomics, and computational biology by maintaining a comprehensive, non-redundant, well-organized and freely available protein sequence database. Since 1988 the database has been maintained collaboratively by PIR-International, an international association of data collection centers cooperating to develop this resource during a period of explosive growth in new sequence data and new computer technologies. The PIR Protein Sequence Database entries are classified into superfamilies, families and homology domains, for which sequence alignments are available. Full-scale family classification supports comparative genomics research, aids sequence annotation, assists database organization and improves database integrity. The PIR WWW server supports direct on-line sequence similarity searches, information retrieval, and knowledge discovery by providing the Protein Sequence Database and other supplementary databases. Sequence entries are extensively cross-referenced and hypertext-linked to major nucleic acid, literature, genome, structure, sequence alignment and family databases. The weekly release of the Protein Sequence Database can be accessed through the PIR Web site. The quarterly release of the database is freely available from our anonymous FTP server and is also available on CD-ROM with the accompanying ATLAS database search program.

Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives. Current database management systems do not provide a convenient means for representing or manipulating this type of uncertainty. In this paper we extend traditional systems to explicitly handle uncertainty in data values. We propose two index structures for efficiently searching uncertain categorical data, one based on the R-tree and another based on an inverted index structure. Using these structures, we provide a detailed description of the probabilistic equality queries they support. Experimental results using real and synthetic datasets demonstrate how these index structures can effectively improve the performance of queries through the use of internal probabilistic information.

Database integrity has two complementary components: validity , which guarantees that all false information is excluded from the database, and completeness , which guarantees that all true information is included in the database. This article describes a uniform model of integrity for relational databases, that considers both validity and completeness. To a large degree, this model subsumes the prevailing model of integrity (i.e., integrity constraints). One of the features of the new model is the determination of the integrity of answers issued by the database system in response to user queries. To users, answers that are accompanied with such detailed certifications of their integrity are more meaningful. First, the model is defined and discussed. Then, a specific mechanism is described that implements this model. With this mechanism, the determination of the integrity of an answer is a process analogous to the determination of the answer itself.

Reasoning on queries is a basic problem both in knowledge representation and databases. A fundamental form of reasoning on queries is checking containment, i.e., verifying whether one query yields necessarily a subset of the result of another query. Query containment is crucial in several contexts, such as query optimization, knowledge base verification, information integration, database integrity checking, and cooperative answering.

Background: Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.

Polymorphism in microRNA Target Site (PolymiRTS) database is a collection of naturally occurring DNA variations in putative microRNA target sites. PolymiRTSs may affect gene expression and cause variations in complex phenotypes. The database integrates sequence polymorphism, phenotype and expression microarray data, and characterizes PolymiRTSs as potential candidates responsible for the quantitative trait locus (QTL) effects. It is a resource for

Corporate databases are potentially rich sources of new and valuable knowledge. Various approaches to "discovering" or "mining" such knowledge have been proposed. We identify an important and previously ignored discovery task, data archaeology. Data archaeology is a skilled human task, in which the knowledge sought depends on the goals of the analyst, cannot be specified in advance, and emerges only through an iterative process of data segmentation and analysis. We describe a system that supports the data archaeologist with a natural, objectoriented representation of an application domain; a powerful query language and database translation routin~s; and an easy-to-use and flexible user interface that supports interactive exploration. A formal knowledge representation system provides the core technology that facilitates database integration, querying, and the reuse of queries and query results.

This paper reports on current research utilising agent based methodologies in order to provide solutions in autonomous map generalisation. The research is in pursuit of systems able to support the derivation of multi scaled products from a single detailed database with minimal human intervention in the map compilation process. Such research has important implications for automated conflation (multiple database integration),and is in response to the huge growth in provision of digital map data over the Internet , coupled with a broadening community of map users who wish to visualise information in a variety of ways but who have little cartographic skill.

Schema integration is important in two contexts, logical database design (in centralized DBMS) and global schema design (in distributed DBMS). Performing an integration on real-life schemas without a tool can be very difficult, tedious and error prone. We have developed an interactive tool to assist database designers and administrators (DDA) in integrating schemas. It collects the information required for integration from a DDA, performs essential bookkeeping, and integrates schemas according to the semantics provided. This paper presents the capabilities of this tool by discussing
the integration methodology and the user interface of the tool.

Keywords: schema integration, view integration, database design and integration, conceptual data model, ECR model, interactive tool,
database designer and administrator

Developing intelligent systems to integrate numerous, autonomous and heterogeneous data sources in order to give end users an uniform query interface is a great challenging issue. The process of constructing a global schema of the integrated system is usually done manually. This is due to the presence of semantic and schematic heterogeneities among schemas of sources. In most cases, sources do not contain enough knowledge to help in solving these heterogeneities and then generating the global schema. In this paper, we present an ontology-driven integration approach called a priori approach. Its originality is that each data source participating in the integration process contains an ontology that defines the meaning of its own data. This approach ensures the automation of the integration process when all sources reference a shared ontology, and possibly extend it by adding their own concept specializations. We present two integration algorithms where (1) the shared ontology may be extended during the integration process, and (2) the instances of local sources are projected onto the shared ontology. Finally, we show that this theory allows to integrate automatically electronic catalogues into corporate engineering databases using the PLIB ontology model. #

ONTOFUSION is an ontology-based system designed for biomedical database integration. It is based on two processes: mapping and unification. Mapping is a semi-automated process that uses ontologies to link a database schema with a conceptual framework-named virtual schema. There are three methodologies for creating virtual schemas, according to the origin of the domain ontology used: (1) top-down-e.g. using an existing ontology, such as the UMLS or Gene Ontology-, (2) bottom-up-building a new domain ontology-and (3) a hybrid combination. Unification is an automated process for integrating ontologies and hence the database to which they are linked. Using these methods, we employed ONTOFUSION to integrate a large number of public genomic and clinical databases, as well as biomedical ontologies.

Here we report on recent developments at the EBI SRS server (http://srs.ebi.ac.uk). SRS has become an integration system for both data retrieval and sequence analysis applications. The EBI SRS server is a primary gateway to major databases in the field of molecular biology produced and

In the new and emerging Agile Manufacturing paradigm, where multiple firms cooperate under flexible virtual enterprise structures, there exists much need for a mechanism to manage and control information flow among collaborating partners. In response to this pressing need, this paper addresses the design and implementation of an agile manufacturing information system integrating manufacturing databases dispersed at various partner sites. We propose a framework in which: (1) Information is modeled in a hierarchical fashion using Object Oriented Methodology (OOM). (2) Information transactions are specified by the workflow hierarchy consisting of partner workflows. (3) Information flow between partners is controlled by a set of distributed Workflow Managers (WM) interacting with partner knowledge bases, which reflect partner specific information control rules on internal data exchange, as well as inter-partner mutual protocols for joint partner communications. (4)

The sequencing and analysis of ESTs is for now the only practical approach for large-scale gene discovery and annotation in conifers because their very large genomes are unlikely to be sequenced in the near future. Our objective was to produce extensive collections of ESTs and cDNA clones to support manufacture of cDNA microarrays and gene discovery in white spruce (Picea glauca [Moench] Voss). We produced 16 cDNA libraries from different tissues and a variety of treatments, and partially sequenced 50,000 cDNA clones. High quality 3' and 5' reads were assembled into 16,578 consensus sequences, 45% of which represented full length inserts. Consensus sequences derived from 5' and 3' reads of the same cDNA clone were linked to define 14,471 transcripts. A large proportion (84%) of the spruce sequences matched a pine sequence, but only 68% of the spruce transcripts had homologs in Arabidopsis or rice. Nearly all the sequences that matched the Populus trichocarpa genome (...

The incidence of extreme precipitation has increased with the exacerbation of worldwide climate disruption. We hypothesize an association between precipitation and the distribution patterns that would affect the endemic burden of 8 infectious diseases in Taiwan, including water-and vector-borne infectious diseases. A database integrating daily precipitation and temperature, along with the infectious disease case registry for all 352 townships in the main island of Taiwan was analysed for the period from 1994 to 2008. Four precipitation levels, ,130 mm, 130-200 mm, 200-350 mm and .350 mm, were categorized to represent quantitative differences, and their associations with each specific disease was investigated using the Generalized Additive Mixed Model and afterwards mapped on to the Geographical Information System. Daily precipitation levels were significantly correlated with all 8 mandatory-notified infectious diseases in Taiwan. For water-borne infections, extreme torrential precipitation (.350 mm/day) was found to result in the highest relative risk for bacillary dysentery and enterovirus infections when compared to ordinary rain (,130 mm/day). Yet, for vector-borne diseases, the relative risk of dengue fever and Japanese encephalitis increased with greater precipitation only up to 350 mm. Differential lag effects following precipitation were statistically associated with increased risk for contracting individual infectious diseases. This study's findings can help health resource sector management better allocate medical resources and be better prepared to deal with infectious disease outbreaks following future extreme precipitation events.

The problem of merging multiple sources information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, expert opinion pooling, etc. Recently, several approaches have been proposed to merge propositional bases, or sets of (non-prioritized) goals. These approaches are in general semantically defined. Like in belief revision, they use implicit priorities, generally based on Dalal's distance, for merging the propositional bases and return a new propositional base as a result. An immediate consequence of the generation of a propositional base is the impossibility of decomposing and iterating the fusion process in a coherent way with respect to priorities since the underlying ordering is lost. This paper presents a general approach for fusing prioritized bases, both semantically and syntactically, when priorities are represented in the possibilistic logic framework. Different classes of merging operators are considered depending on whether the sources are consistent, conflicting, redundant or independent. We show that the approaches which have been recently proposed for merging propositional bases can be embedded in this setting. The result is then a prioritized base, and hence the process can be coherently decomposed and iterated. Moreover, this encoding provides a syntactic counterpart for the fusion of propositional bases.

Background Traditional Chinese Medicine (TCM), a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature. Methods TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effecters and effects. Results We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at http://tcm.lifescience.ntu.edu.tw/. Conclusion TCMGeneDIT is a unique database that offers diverse association information on TCMs. This database integrates TCMs with biomedical studies that would facilitate clinical research and elucidate the possible therapeutic mechanisms of TCMs and gene regulations.

To catalogue data on chromosomal aberrations in cancer derived from emerging molecular cytogenetic techniques and to integrate these data with genome maps, we have established two resources, the NCI and NCBI SKY/M-FISH & CGH Database, and the Cancer Chromosomes database. The goal of the former is to allow investigators to submit and analyze clinical and research cytogenetic data. It contains a karyotype parser tool, which automatically converts the ISCN shortform karyotype into an internal representation displayed in detailed form and as a colored ideogram with band overlay, and also contains a tool to compare CGH profiles from multiple cases. The Cancer Chromosomes database integrates the SKY/M-FISH & CGH Database with the Mitelman Database of Chromosome Aberrations in Cancer, and the Recurrent Chromosome Aberrations in Cancer database. These three datasets can now be searched seamlessly by use of the Entrez search and retrieval system for chromosome aberrations, clinical data, and reference citations. Common diagnoses, anatomic sites, chromosome breakpoints, junctions, numerical and structural abnormalities, and bands gained and lost among selected cases can be compared by use of the "similarity" report. Because the model used for CGH data is a subset of the karyotype data, it is now possible to examine the similarities between CGH results and karyotypes directly. All chromosomal bands are directly linked to the Entrez Map Viewer database, providing integration of cytogenetic data with the sequence assembly. These resources, developed as a part of the Cancer Chromosome Aberration Project (CCAP) initiative, aid the search for new cancer-associated genes and foster insights into the causes and consequences of genetic alterations in cancer.

During the process of updating a database, two interrelated problems could arise. On one hand, when an update is applied to the database, integrity constraints could become violated, thus falsifying database consistency. In this case, the integrity constraint maintenance approach tries to obtain additional updates to be applied to re-establish database consistency. On the other hand, when an update request consist on updating some derived predicate, a view updating mechanism must be applied to translate the update request into correct updates on the underlying base facts.

Every information system incorporates a database component, and a frequent activity of users of information systems is to present it with queries. These queries reflect the presuppositions of their authors about the system and the information it contains. With most query processors, queries that are based on erroneous presuppositions often result in null answers. These fake nulls are misleading, since they do not point out the user's erroneous presuppositions (and can even be interpreted as their affirmation). This article describes the SEAVE mechanism for extracting presuppositions from queries and verifying their correctness. The verification is done against three repositories of information: the actual data, their integrity constraints, and their completeness assertions. Consequently, queries that reflect erroneous presuppositions are answered with informative messages instead of null answers, and user-system communication is thus improved (an aspect that is particularly important in systems that often are accessed by naive users). First, the principles of SEAVE are described abstractly. Then, specific algorithms for implementing it with relational databases are presented, including a new method for storing knowledge and an efficient algorithm for processing queries against the knowledge.

The identification of molecular signatures predictive of clinical behavior and outcome in brain tumors has been the focus of many studies in the recent years. Despite the wealth of data that are available in the public domain on alterations in the genome, epigenome and transcriptome of brain tumors, the underlying molecular mechanisms leading to tumor initiation and progression remain largely unknown. Unfortunately, most of these data are scattered in multiple databases and supplementary materials of publications, thus making their retrieval, evaluation, comparison and visualization a rather arduous task. Here we report the development and implementation of an open access database (BTECH), a community resource for the deposition of a wide range of molecular data derived from brain tumor studies. This comprehensive database integrates multiple datasets, including transcript profiles, epigenomic CpG methylation data, DNA copy number alterations and structural chromosomal rearrangements, tumor-associated gene lists, SNPs, genomic features concerning Alu repeats and general genomic annotations. A genome browser has also been developed that allows for the simultaneous visualization of the different datasets and the various annotated features. Besides enabling an integrative view of diverse datasets through the genome browser, we also provide links to the original references for users to have a more accurate understanding of each specific dataset. This integrated platform will facilitate uncovering interactions among genetic and epigenetic factors associated with brain tumor development. BTECH is freely available at http:// cmbteg.childrensmemorial.org/.

Inference has been a longstanding issue in database security, and inference control, aiming to curb inference, provides an extra line of defense to the confidentiality of databases by complementing access control. However, in traditional inference control architecture, database server is a crucial bottleneck, as it enforces highly computation-intensive auditing for all users who query the protected database. As a result, most auditing methods, though rigorously studied, are not practical for protecting large-scale real-world database systems. In this paper, we shift this paradigm by proposing a new inference control architecture, entrusting inference control to each user's platform that is equipped with trusted computing technology. The trusted computing technology is designed to attest the state of a user's platform to the database server, so as to assure the server that inference control could be enforced as prescribed. A generic protocol is proposed to formalize the interactions between the user's platform and database server. The authentication property of the protocol is formally proven. Since inference control is enforced in a distributed manner, our solution avoids the bottleneck in the traditional architecture, thus can potentially support a large number of users making queries.

Taking advantage of recent advances in automated theorem proving, we present a new method for determining whether database transactions preserve integrity constraints. We consider check constraints and referential-integrity constraints—extracted from SQL table declarations—and application-level invariants expressed as formulas of first-order logic. Our motivation is to use static analysis of database transactions at development time, to catch bugs early, or during deployment, to allow only integrity-preserving stored procedures to be accepted. We work in the setting of a functional multi-tier language, where functional code is compiled to SQL that queries and updates a relational database. We use refinement types to track constraints on data and the underlying database. Our analysis uses a refinement-type checker, which relies on recent highly efficient SMT algorithms to check proof obligations. Our method is based on a list-processing semantics for an SQL fragment within the functional language, and is illustrated by a series of examples.

We discuss recent seasonal and interannual variations of ice cover and lake surface level in the Aral Sea from satellite data for 1992–2006. First, we provide an overview of the evolution of the Aral Sea's environmental conditions, hydrological and ice regime, existing observations and current state of the scientific research. Desiccation of the Aral Sea led to disappearance of the infrastructure in the coastal zone, including meteorological and sea level gauge stations. The current lack of reliable in-situ measurements and time series for sea level and ice cover parameters since mid-1980s can be partly overcome with radar altimeter and microwave satellite observations that provide reliable, frequent, regular and weather-independent data. In our study, we use radar altimeter data from TOPEX/Poseidon, Jason-1, ENVISAT and GFO satellites, as well as the Special Sensor Microwave/Imager (SSM/I) radiometer. An ice discrimination method, based on the synergy of active and passive data from the four altimetric missions and SSM/I, is proposed and applied to the entire satellite dataset to define the specific dates of ice events for 1992–2006. We then analyse the evolution of the sea level in the Large and Small Aral sea and Sarykamysh lake. For this purpose, we compare time series from several sources (Hydroweb, USDA Reservoir Database, Integrated Satellite Altimetry Data Base and others), perform an intercomparison of the available observations and discuss the reasons for potential differences. Using the data from the four altimetric retrackers for ENVISAT, we also estimate how the presence of ice could affect the altimeter range measures. We estimate the associated uncertainties and provide recommendations for adjusting sea level time series for altimeters where only ocean retracker (T/P, Jason-1, GFO) is present.

Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel (1989) showed that uncertain information can be generated by the mapping of conflicting attributes to a common domain, based on some domain knowledge. The authors show that uncertain information can also arise when the database integration process requires information not directly represented in the component databases, but can be obtained through some summary of data. They therefore propose ...

In this paper, we describe OntoFusion, a database integration system. This system has been designed to provide unified access to multiple, heterogeneous biological and medical data sources that are publicly available over Internet. Many of these databases do not offer a direct connection, and inquiries must be made via Web forms, returning results as HTML pages. A special module in the OntoFusion system is needed to integrate these public 'Web-based' databases. Domain ontologies are used to do this and provide database mapping and unification. We have used the system to integrate seven significant and widely used public biomedical databases: OMIM, PubMed, Enzyme, Prosite and Prosite documentation, PDB, SNP, and InterPro. A case study is detailed in depth, showing system performance. We analyze the system's architecture and methods and discuss its use as a tool for biomedical researchers.

Background: Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.

We show how to formalise different kinds of loop constructs within the refinement calculus, and how to use this formalisation to derive general transformation rules for loop constructs. The emphasis is on using algebraic methods for reasoning about equivalence and refinement of loop constructs, rather than operational ways of reasoning about loops in terms of their execution sequences. We apply the algebraic reasoning techniques to derive a collection of transformation rules for action systems and for guarded loops. These include transformation rules that have been found important in practical program derivations: data refinement and atomicity refinement of action systems; and merging, reordering, and data refinement of loops with stuttering transitions.

A key aspect of interoperation among data-intensive systems involves the mediation of metadata and ontologies across database boundaries. One way to achieve such mediation between a local database and a remote database is to fold remote metadata into the local metadata, thereby creating a common platform through which information sharing and exchange becomes possible. Schema implantation and semantic evolution, our approach to the metadata folding problem, is a partial database integration scheme in which remote and local (meta)data are integrated in a stepwise manner over time. We introduce metadata implantation and stepwise evolution techniques to interrelate database elements in different databases, and to resolve conflicts on the structure and semantics of database elements (classes, attributes, and individual instances). We employ a semantically rich canonical data model, and an incremental integration and semantic heterogeneity resolution scheme. In our approach, relationships between local and remote information units are determined whenever enough knowledge about their semantics is acquired. The metadata folding problem is solved by implanting remote database elements into the local database, a process that imports remote database elements into the local database environment, hypothesizes the relevance of local and remote classes, and customizes the organization of remote metadata. We have implemented a prototype system and demonstrated its use in an experimental neuroscience environment.

The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies.

Many government, academic and research institutions collect environmental data that are relevant to understanding the relationship between environmental exposures and human health. Integrating these data with health outcome data presents new challenges that are important to consider to improve our effective use of environmental health information. Our objective was to identify the common themes related to the integration of environmental and health data, and suggest ways to address the challenges and make progress toward more effective use of data already collected, to further our understanding of environmental health associations in the Great Lakes region. Environmental and human health databases were identified and reviewed using literature searches and a series of one-on-one and group expert consultations. Databases identified were predominantly environmental stressors databases, with fewer found for health outcomes and human exposure. Nine themes or factors that impact integrati...

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 8 ( 2 0 1 2 ) 90-101 Public health information systems Database integration Cervical cancer a b s t r a c t This paper aims at to present the integration of the files of the Brazilian Cervical Cancer Information System (SISCOLO) in order to identify all women in the system. SISCOLO has the exam as the unit of observation and the women are not uniquely identified. It has two main tables: histology and cytology, containing the histological and cytological examinations of women, respectively. In this study, data from June 2006 to December 2009 were used. Each table was linked with itself and with the other through record linkage methods. The integration identified 6236 women in the histology table and 1,678,993 in the cytology table. 5324 women from the histology table had records in the cytology table. The sensitivities were above 90% and the specificities and precisions near 100%. This study showed that it is possible to integrate SISCOLO to produce indicators for the evaluation of the cervical cancer screening programme taking the woman as the unit of observation. (S.M. Freire).