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PubMed2004

CRAVE: a database, middleware and visualization system for phenotype ontologies.

MOTIVATION: A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. RESULTS: CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.

Algorithms↗

PubMed2002

SELEX_DB: a database on in vitro selected oligomers adapted for recognizing natural sites and for analyzing both SNPs and site-directed mutagenesis data.

SELEX_DB is an online resource containing both the experimental data on in vitro selected DNA/RNA oligomers (aptamers) and the applets for recognition of these oligomers. Since in vitro experimental data are evidently system-dependent, the new release of the SELEX_DB has been supplemented by the database SYSTEM storing the experimental design. In addition, the recognition applet package, SELEX_TOOLS, applying in vitro selected data to annotation of the genome DNA, is accompanied by the cross-validation test database CROSS_TEST discriminating the sites (natural or other) related to in vitro selected sites out of random DNA. By cross-validation testing, we have unexpectedly observed that the recognition accuracy increases with the growth of homology between the training and test sets of protein binding sequences. For natural sites, the recognition accuracy was lower than that for the nearest protein homologs and higher than that for distant homologs and non-homologous proteins binding the common site. The current SELEX_DB release is available at http://wwwmgs.bionet.nsc.ru/mgs/systems/selex/.

Binding Sites↗

PubMedDate not supplied

A novel visualization model for web search results.

This paper presents an interactive visualization system, named WebSearchViz, for visualizing the Web search results and acilitating users' navigation and exploration. The metaphor in our model is the solar system with its planets and asteroids revolving around the sun. Location, color, movement, and spatial distance of objects in the visual space are used to represent the semantic relationships between a query and relevant Web pages. Especially, the movement of objects and their speeds add a new dimension to the visual space, illustrating the degree of relevance among a query and Web search results in the context of users' subjects of interest. By interacting with the visual space, users are able to observe the semantic relevance between a query and a resulting Web page with respect to their subjects of interest, context information, or concern. Users' subjects of interest can be dynamically changed, redefined, added, or deleted from the visual space.

Algorithms↗

PubMed2004

Techniques for optimization of queries on integrated biological resources.

Today, scientific data are inevitably digitized, stored in a wide variety of formats, and are accessible over the Internet. Scientific discovery increasingly involves accessing multiple heterogeneous data sources, integrating the results of complex queries, and applying further analysis and visualization applications in order to collect datasets of interest. Building a scientific integration platform to support these critical tasks requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web, as well as data that are locally materialized in warehouses or generated by software. The lack of efficiency of existing approaches can significantly affect the process with lengthy delays while accessing critical resources or with the failure of the system to report any results. Some queries take so much time to be answered that their results are returned via email, making their integration with other results a tedious task. This paper presents several issues that need to be addressed to provide seamless and efficient integration of biomolecular data. Identified challenges include: capturing and representing various domain specific computational capabilities supported by a source including sequence or text search engines and traditional query processing; developing a methodology to acquire and represent semantic knowledge and metadata about source contents, overlap in source contents, and access costs; developing cost and semantics based decision support tools to select sources and capabilities, and to generate efficient query evaluation plans.

Algorithms↗

PubMedDate not supplied

Effective drug-allergy checking: methodological and operational issues.

Adverse drug events cause a large number of injuries, and adverse events caused by medications administered in the face of known allergies represent an important preventable cause of patient harm. Computerized systems can effectively prevent reactions due to known allergies, but building an effective allergy prevention feature is challenging and presents many interesting informatics issues that have both methodological and operational implications. In this paper, we present the experiences from one large delivery system in delivering allergy-related decision support, discuss some of the different approaches that we have used, and then propose a future approach. We also discuss the methodological, behavioral, and operational issues that have arisen which have a major impact on success. Key factors in drug-allergy checking include storing patient allergy data in a single common repository, representing allergy data using suitable terminologies and creating groups of allergies for inferencing purposes, being judicious about which allergy warnings to display, conveying the reaction that the patient has experienced when exposed to the drug to inform the provider of the importance of the warning, and perhaps most important, implementing strategies to optimize the likelihood that allergy information will be entered.

Database Management Systems↗

PubMed2006

RAMEDIS: the rare metabolic diseases database.

UNLABELLED: The RAMEDIS system is a platform-independent, web-based information system for rare diseases based on individual case reports. It was developed in close cooperation with clinical partners and collects information on rare metabolic diseases in extensive detail (e.g. symptoms, laboratory findings, therapy and genetic data). This combination of clinical and genetic data enables the analysis of genotype-phenotype correlations. By using largely standardised medical terms and conditions, the contents of the database are easy to compare and analyse. In addition, a convenient graphical user interface is provided by every common web browser. RAMEDIS supports an extendable number of different genetic diseases and enables cooperative studies. Furthermore, use of RAMEDIS should lead to advances in epidemiology, integration of molecular and clinical data, and generation of rules for therapeutic intervention and identification of new diseases. AVAILABILITY: RAMEDIS is available from http://www.ramedis.de CONTACT: Thoralf Töpel (thoralf.toepel@uni-bielefeld.de).

Computational Biology↗

PubMed2002

Information extraction for enhanced access to disease outbreak reports.

Document search is generally based on individual terms in the document. However, for collections within limited domains it is possible to provide more powerful access tools. This paper describes a system designed for collections of reports of infectious disease outbreaks. The system, Proteus-BIO, automatically creates a table of outbreaks, with each table entry linked to the document describing that outbreak; this makes it possible to use database operations such as selection and sorting to find relevant documents. Proteus-BIO consists of a Web crawler which gathers relevant documents; an information extraction engine which converts the individual outbreak events to a tabular database; and a database browser which provides access to the events and, through them, to the documents. The information extraction engine uses sets of patterns and word classes to extract the information about each event. Preparing these patterns and word classes has been a time-consuming manual operation in the past, but automated discovery tools now make this task significantly easier. A small study comparing the effectiveness of the tabular index with conventional Web search tools demonstrated that users can find substantially more documents in a given time period with Proteus-BIO.

Abstracting and Indexing↗

PubMed2003

Terminology-driven mining of biomedical literature.

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.

Abbreviations as Topic↗

PubMed2005

IMGT, the international ImMunoGeneTics information system.

The international ImMunoGeneTics information system (IMGT) (http://imgt.cines.fr), created in 1989, by the Laboratoire d'ImmunoGenetique Moleculaire LIGM (Universite Montpellier II and CNRS) at Montpellier, France, is a high-quality integrated knowledge resource specializing in the immunoglobulins (IGs), T cell receptors (TRs), major histocompatibility complex (MHC) of human and other vertebrates, and related proteins of the immune systems (RPI) that belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF). IMGT includes several sequence databases (IMGT/LIGM-DB, IMGT/PRIMER-DB, IMGT/PROTEIN-DB and IMGT/MHC-DB), one genome database (IMGT/GENE-DB) and one three-dimensional (3D) structure database (IMGT/3Dstructure-DB), Web resources comprising 8000 HTML pages (IMGT Marie-Paule page), and interactive tools. IMGT data are expertly annotated according to the rules of the IMGT Scientific chart, based on the IMGT-ONTOLOGY concepts. IMGT tools are particularly useful for the analysis of the IG and TR repertoires in normal physiological and pathological situations. IMGT is used in medical research (autoimmune diseases, infectious diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research, biotechnology related to antibody engineering (phage displays, combinatorial libraries, chimeric, humanized and human antibodies), diagnostics (clonalities, detection and follow up of residual diseases) and therapeutical approaches (graft, immunotherapy and vaccinology). IMGT is freely available at http://imgt.cines.fr.

Animals↗

PubMed2006

Dynamic exploration and editing of KEGG pathway diagrams.

MOTIVATION: The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway database is a very valuable information resource for researchers in the fields of life sciences. It contains metabolic and regulatory processes in the form of wiring diagrams, which can be used for browsing and information retrieval as well as a base for modeling and simulation. Thus it helps in understanding biological processes and higher-order functions of biological systems. Currently the KEGG website uses semi-static visualizations for the presentation and navigation of its pathway information. While this visualization style offers a good pathway presentation and navigation, it does not provide some of the possibilities related to dynamic visualizations, most importantly, the creation and visualization of user-specific pathways. RESULTS: This paper presents methods for the dynamic visualization, interactive navigation and editing of KEGG pathway diagrams. These diagrams, given as KEGG Markup Language (KGML) files, can be visually explored using novel approaches combining semi-static and dynamic visualization, but also edited or even newly created and then exported into KGML files. AVAILABILITY: KGML-ED, a program implementing the presented methods, is available free of charge to the scientific community at http://kgml-ed.ipk-gatersleben.de.

Algorithms↗

PubMed2004

QIS: A framework for biomedical database federation.

Query Integrator System (QIS) is a database mediator framework intended to address robust data integration from continuously changing heterogeneous data sources in the biosciences. Currently in the advanced prototype stage, it is being used on a production basis to integrate data from neuroscience databases developed for the SenseLab project at Yale University with external neuroscience and genomics databases. The QIS framework uses standard technologies and is intended to be deployable by administrators with a moderate level of technological expertise: It comes with various tools, such as interfaces for the design of distributed queries. The QIS architecture is based on a set of distributed network-based servers, data source servers, integration servers, and ontology servers, that exchange metadata as well as mappings of both metadata and data elements to elements in an ontology. Metadata version difference determination coupled with decomposition of stored queries is used as the basis for partial query recovery when the schema of data sources alters.

Computer Communication Networks↗

PubMed2003

Towards optimal views of proteins.

MOTIVATION: Graphical representations of proteins in online databases generally give default views orthogonal to the PDB file coordinate system. These views are often uninformative in terms of protein structure and/or function. Here we discuss the development of a simple automatic algorithm to provide a 'good' view of a protein domain with respect to its structural features. RESULTS: We used dimension reduction with the preservation of topology (using Kohonen's self organising map) to map 3D carbon alpha coordinates into 2D. The original protein structure was then rotated to the view which corresponded most closely to the 2D mapping. This procedure, which we call OVOP, was evaluated in a public blind trial on the web against random views and a 'flattest' view. The OVOP views were consistently rated 'better' than the other views by our volunteers. AVAILABILITY: The source code is available from the OVOP homepage: http://www.sbc.su.se/~oscar/ovop.

Algorithms↗

PubMed2002

Biological information: making it accessible and integrated (and trying to make sense of it).

The availability of the genome sequences of human and mouse, human sequence variation data and other large genetic data sets will lead to a revolution in understanding of the human machine and the treatment of its diseases. The success of the international genome sequencing consortiums shows what can be achieved by well coordinated large scale public domain projects and the benefits of data access to all. It is already clear that the availability of this sequence is having a huge impact on research worldwide. Complete genome sequences provide a framework to pull all biological data together such that each piece has the potential to say something about biology as a whole. Biology is too complex for any organisation to have a monopoly of ideas or data, so the collection, analysis and access to this data can be contributed to by research institutes around the world. However, although it is possible for all this data to be accessible to all through the internet, the more organisations provide data or analysis separately, the harder it becomes for anyone to collect and integrate the results. To address these problems of intergration of data, open standards for biological data exchange, such as the 'Distributed Annotation System' (DAS) are being developed and bioinformatics (Dowell et al., 2001) as a whole is now being strongly driven by the open source software (OSS) model for collaborative software development (Hubbard and Birney, 1999). The leading provider of human genome annotation, the Ensembl project (http://www.ensembl.org), is entirely an OSS project and has been widely adopted by academic and commerical organisations alike (Hubbard et al., 2002). Accurate automatic annotation of features such as genes in vertebrate genomes currently relies on supporting evidence in the form of homologies to mRNAs, ESTs or protein. However, it appears that sufficient high quality experimentally curated annotation now exists to be used as a substrate for machine learning algorithms to create effective models of biological signal sequences (Down and Hubbard, 2002). Is there hope for ab initio prediction methods after all?

Chromosome Mapping↗

PubMedDate not supplied

VLOD: high-fidelity walkthrough of large virtual environments.

We present visibility computation and data organization algorithms that enable high-fidelity walkthroughs of large 3D geometric data sets. A novel feature of our walkthrough system is that it performs work proportional only to the required detail in visible geometry at the rendering time. To accomplish this, we use a precomputation phase that efficiently generates per cell vLOD: the geometry visible from a view-region at the right level of detail. We encode changes between neighboring cells' vLODs, which are not required to be memory resident. At the rendering time, we incrementally construct the vLOD for the current view-cell and render it. We have a small CPU and memory requirement for rendering and are able to display models with tens of millions of polygons at interactive frame rates with less than one pixel screen-space deviation and accurate visibility.

Algorithms↗

PubMed2003

Initial large-scale exploration of protein-protein interactions in human brain.

Study of protein interaction networks is crucial to post-genomic systems biology. Aided by high-throughput screening technologies, biologists are rapidly accumulating protein-protein interaction data. Using a random yeast two-hybrid (R2H) process, we have performed large-scale yeast two-hybrid searches with approximately fifty thousand random human brain cDNA bait fragments against a human brain cDNA prey fragment library. From these searches, we have identified 13,656 unique protein-protein interaction pairs involving 4,473 distinct known human loci. In this paper, we have performed our initial characterization of the protein interaction network in human brain tissue. We have classified and characterized all identified interactions based on Gene Ontology (GO) annotation of interacting loci. We have also described the "scale-free" topological structure of the network.

Brain↗

PubMed2006

A flexible integration and visualisation system for biomarker discovery.

Biological data have accumulated at an unprecedented pace as a result of improvements in molecular technologies. However, the translation of data into information, and subsequently into knowledge, requires the intricate interplay of data access, visualisation and interpretation. Biological data are complex and are organised either hierarchically or non-hierarchically. For non-hierarchically organised data, it is difficult to view relationships among biological facts. In addition, it is difficult to make changes in underlying data storage without affecting the visualisation interface. Here, we demonstrate a platform where non-hierarchically organised data can be visualised through the application of a customised hierarchy incorporating medical subject headings (MeSH) classifications. This platform gives users flexibility in updating and manipulation. It can also facilitate fresh scientific insight by highlighting biological impacts across different hierarchical branches. An example of the integration of biomarker information from the curated Proteome database using MeSH and the StarTree visualisation tool is presented.

Algorithms↗

PubMed2006

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid.

MOTIVATION: The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS: At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY: The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.

Biomarkers, Tumor↗

PubMed2004

Parallel and distributed methods for incremental frequent itemset mining.

Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.

Algorithms↗