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Social computing technologies are becoming increasingly popular as it allows people to create and share their own content. Given that most social computing technologies are limited to fixed environments, this paper outlines an exploratory study which investigates the characteristics of people's creative information sharing process; identifying user needs and difficult scenarios during the process, focusing particularly on mobile scenarios. The results give an indication about people's potential needs to create and share whilst mobile. It describes the characteristics of creative information sharing process and suggests that supporting the process of information sharing by harnessing context-aware elements could be a potential solution. [ABSTRACT FROM AUTHOR]

In the last decade, many projects have tried to deal with the integration of biological resources. Web portals have flourished online, each providing data from a public provider. Although these online resources are available with a set of manipulation tools, scientists, researchers, and students often have to shift from one resource to another to accomplish a particular task. Making a rich tool set available along with a variety of databases, data formats, and computational capabilities is a complex task. It requires building a versatile environment for data integration, data manipulation, and data storage. In this paper, we study the requirements and report the architectural design of a web application, code named SWAMI, which aims at integrating a rich tool set and a variety of biological databases. The suggested architecture is highly scalable in terms of adding databases and new manipulation tools. [ABSTRACT FROM AUTHOR]

Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone. [ABSTRACT FROM AUTHOR]

We describe our biomedical concept annotator designed for online environments, CONANN, which takes a biomedical source phrase and finds the best-matching biomedical concept from a domain resource. Domain concepts are defined in resources such as the United States National Library of Medicine's Unified Medical Language System Metathesaurus. CONANN uses an incremental filtering approach to narrow down a list of candidate phrases before deciding on a best match. We show that this approach has the advantage of improving annotation speed over an existing state-of-the-art concept annotator, facilitating the use of concept annotation in online environments. Our main contributions are 1) the design of a phrase-unit concept annotator more readily usable in online environments than existing systems, 2) the introduction of a model which uses semantically focused words in a given ontology (e.g., UMLS) to measure coverage, called Inverse Phrase Frequency, and 3) the use of two different filters to measure coverage and coherence between a source phrase and a domain-specific candidate phrase. An intrinsic evaluation comparing CONANN's concept output to a state-of-the-art concept annotator shows our system has an annotation precision ranging from 90% for exact match concept to 95% for relaxed concept matching while average phrase annotation time is eighteen times faster. In addition, an extrinsic evaluation using the generated concepts in a text summarization task shows no significant degradation when using CONANN. [ABSTRACT FROM AUTHOR]

This paper presents the LSLink (or Life Science Link) methodology that provides users with a set of tools to explore the rich Web of interconnected and annotated objects in multiple repositories, and to identify meaningful associations. Consider a physical link between objects in two repositories, where each of the objects is annotated with controlled vocabulary (CV) terms from two ontologies. Using a set of LSLink instances generated from a background dataset of knowledge we identify associations between pairs of CV terms that are potentially significant and may lead to new knowledge. We develop an approach based on the logarithm of the odds (LOD) to determine a confidence and support in the associations between pairs of CV terms. Using a case study of Entrez Gene objects annotated with GO terms linked to PubMed objects annotated with MeSH terms, we describe a user validation and analysis task to explore potentially significant associations. [ABSTRACT FROM AUTHOR]

Data integration projects in the life sciences often gather data on a particular subject from multiple sources. Some of these sources overlap to a certain degree. Therefore, integrated search results may be supported by one, few, or all data sources. To reflect these differences, results should be ranked according to the number of data sources that support them. How such a ranking should look like is not clear per se. Either, results supported by only few sources are ranked high because this information is potentially new, or such results are ranked low because the strength of evidence supporting them is limited. We present two scoring schemes to rank search results in the integrated protein annotation database Columba. We define a surprisingness score, preferring results supported by few sources, and a confidence score, preferring frequently encountered information. Unlike many other scoring schemes our proposal is purely data-driven and does not require users to specify preferences among sources. Both scores take the concrete overlaps of data sources into account and do not presume statistical independence. We show how our schemes have been implemented efficiently using SQL. [ABSTRACT FROM AUTHOR]

Information about small genetic variations in organisms, known as single nucleotide polymorphism (SNPs), is crucial to identify candidate genes that have a role in disease susceptibility, a long-standing research goal in biology. While a number of established public SNP databases are available, the specification of effective techniques for SNP analysis remains an open issue. We describe a secondary SNP database that integrates data from multiple public sources, designed to support various experimental ranking models for SNPs. By prioritizing SNPs within large regions of the genome, scientists are able to rapidly narrow their search for candidate genes. In the paper we describe the ranking models, the data integration architecture, and preliminary experimental results. [ABSTRACT FROM AUTHOR]

Decoding the functional elements in an organism's genome requires the integration of a wide variety of experimental and computational data from a wide range of sources. The location of this data, viewed as sequence features in the genome, must serve as one of the essential organizing principles for this integration. It is therefore important to have a data integration system that takes advantage of this fact. As part of the TESS project, we have developed a grammar-based data integration and pattern search tool, Annotation Grammar and Extraction Language (AnGEL), that follows this principle. AnGEL can represent most of the current work in cis-regulatory module (CRM) modelling in an intuitive way and can process data extracted from a variety of sources simultaneously. Here we describe AnGEL's capabilities and illustrate its use by querying for gene arrangements, CRMs, and protein domain structure. [ABSTRACT FROM AUTHOR]

Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between molecular-biological objects and ontologies. The approach can build on many existing ontology associations for instance objects like sequences and proteins and thus makes heavy use of available domain knowledge. Furthermore, the approach is flexible and extensible since each instance source with associations to the ontologies of interest can contribute to the ontology mapping. We study several approaches to determine the instance-based similarity of ontology categories. We perform an extensive experimental evaluation to use protein associations for different species to match between subontologies of the Gene Ontology and OMIM. We also provide a comparison with metadata-based ontology matching. [ABSTRACT FROM AUTHOR]

Nowadays, ontologies and machine learning constitute two major technologies for domain-specific knowledge extraction which are actively used in knowledge-based systems of different kind including expert systems, decision support systems, knowledge discovery systems, etc. While the aim of these two technologies is the same - the extraction of useful knowledge - little is known about how the two sources of knowledge can be successfully integrated. Today the two technologies are used mainly separate; even though the knowledge extracted by the two is complementary and significant benefits can be obtained if the technologies were integrated. This problem is especially important for biomedicine where relevant data are often naturally complex having large dimensionality and including heterogeneous features, and where a large body of knowledge is available in the form of ontologies. In this paper we propose one approach for improving the performance of machine learning algorithms by integrating the knowledge provided by ontologies. The basic idea is to redefine the concept of similarity for complex heterogeneous data by incorporating available ontological knowledge, creating a bridge between the two technologies. Potential benefits and difficulties of this integration are discussed, two techniques for empirical evaluation and fine-tuning of feature ontologies are described, and an example from the field of paediatric cardiology is given [ABSTRACT FROM AUTHOR]

While a number of scientific workflow systems support data provenance, they primarily focus on collecting and querying provenance for single workflow runs. Scientific research projects, however, typically involve (1) many interrelated workflows (where data from one or more workflow runs are selected and used as input to subsequent runs) and (2) tasks between workflow runs that cannot be fully automated. This paper addresses the need for recording data dependencies across multiple workflow runs and accommodating data management activities performed between runs. We define a new conceptual model for representing project-level provenance based on the notion of project histories and folders, and describe mechanisms to support this model in the collection-oriented modeling and design framework of Kepler. Our approach allows users to conveniently organize their projects and data using the familiar folder-hierarchy metaphor, while at the same time integrating this information with detailed provenance of data products generated via automated scientific workflows. [ABSTRACT FROM AUTHOR]

2D-Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical method to elucidate the chemical structure of molecules. In contrast to 1D-NMR spectra, 2D-NMR spectra correlate the chemical shifts of 1H and 13C simultaneously. To curate or merge large spectra libraries a robust (and fast) duplicate detection is needed. We propose a definition of duplicates with the desired robustness properties mandatory for 2D-NMR experiments. A major gain in runtime performance wrt. previously proposed heuristics is achieved by mapping the spectra to simple discrete objects. We propose several appropriate data transformations for this task. In order to compensate for slight variations of the mapped spectra, we use appropriate hashing functions according to the locality sensitive hashing scheme, and identify duplicates by hash-collisions. [ABSTRACT FROM AUTHOR]

Dataflow repositories are databases containing dataflows and their different runs. We propose a formal conceptual data model for such repositories. Our model includes careful formalisations of such features as complex data manipulation, external service calls, subdataflows, and the provenance of output values. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of bioinformatics service reconciliation in a generic and scalable manner so as to enhance interoperability in a highly evolving field. Using XML as a common representation format, but also supporting existing flat-file representation formats, we propose an approach for the scalable semi-automatic reconciliation of services, possibly invoked from within a scientific workflows tool. Service reconciliation may use the AutoMed heterogeneous data integration system as an intermediary service, or may use AutoMed to produce services that mediate between services. We discuss the application of our approach for the reconciliation of services in an example bioinformatics workflow. The main contribution of this research is an architecture for the scalable reconciliation of bioinformatics services. [ABSTRACT FROM AUTHOR]

The success of mass spectrometry-based proteomics in emerging applications such as biomarker discovery and clinical diagnostics, is predicated substantially on its ability to achieve growing demands for throughput. Support for high throughput implies sophisticated tracking of experiments and the experimental steps, larger amounts of data to be organized and summarized, more complex algorithms for inferring and tracking protein expression across multiple experiments, statistical methods to access data quality, and a streamlined proteomics-centric bioinformatics environment to establish the biological context and relevance of the experimental measurements. This paper presents a bioinformatics platform that was built for an industrial mass spectrometry-based proteomics laboratory focusing on biomarker discovery. The basis of the platform is a robust and scalable information management environment supported by database and workflow management technology that is employed for the integration of heterogeneous data, applications and processes across the entire laboratory workflow. This paper focuses on selected features of the platform which include: (a) a method for improving the accuracy of protein assignment, (b) novel software tools for protein expression analysis that combine differential MS quantitation with tandem MS for peptide identification, and (c) integration of methods to aid the biological relevance and statistical significance of differentially expressed proteins. [ABSTRACT FROM AUTHOR]

In silico experiments have hitherto required ad hoc collections of scripts and programs to process and visualise biological data, consuming substantial amounts of time and effort to build, and leading to tools that are difficult to use, are architecturally fragile and scale poorly. With examples of the systems applied to real biological problems, we describe two complimentary software frameworks that address this problem in a principled manner; $^{\textrm{\small{my}}}$Grid Taverna, a workflow design and enactment environment enabling coherent experiments to be built, and UTOPIA, a flexible visualisation system to aid in examining experimental results. [ABSTRACT FROM AUTHOR]

Life sciences research in general and systems biology in particular have evolved from the simple combination of theoretical frameworks and experimental hypothesis validation to combined sciences of biology/medicine, analytical technology/chemistry, and informatics/statistics/modeling. Integrating these multiple threads of a research project at the technical and data level requires tight control and systematic workflows for data generation, data management, and data evaluation. Systems biology research emphasizes the use of multiple approaches at various molecular and functional levels, making the use of complementing technologies and the collaboration of many researchers a prerequisite. This paper presents B-Fabric, a system developed and running at the Functional Genomics Center Zurich (FGCZ), which provides a core framework for integrating different analytical technologies and data analysis tools. In addition to data capturing and management, B-Fabric emphasizes the need for quality-controlled scientific annotation of analytical data, providing the ground for integrative querying and exploitation of systems biology data. Users interact with B-Fabric through a simple Web portal making the framework flexible in terms of local infrastructure. [ABSTRACT FROM AUTHOR]

In this paper we present the design and implementation of an RNA structural motif database, called RmotifDB. The structural motifs stored in RmotifDB come from three sources: (1) collected manually from biomedical literature; (2) submitted by scientists around the world; and (3) discovered by a wide variety of motif mining methods. We present here a motif mining method in detail. We also describe the interface and search mechanisms provided by RmotifDB as well as techniques used to integrate RmotifDB with the Gene Ontology. The RmotifDB system is fully operational and accessible on the Internet at http://datalab.njit.edu/bioinfo/ [ABSTRACT FROM AUTHOR]

The Allen Brain Atlas (ABA), publicly available at http:// www.brain-map.org, presents the expression patterns of more than 21,500 genes in the adult mouse brain. The project has produced more than 600 Terabytes of cellular level in situ hybridization data whose images have been reconstructed and mapped into whole brain 3D volumes for search and viewing. In this application paper we outline the bioinformatics, data integration, and presentation approach to the ABA and the creation a fully automated high-throughput pipeline to deliver this data set to the Web. [ABSTRACT FROM AUTHOR]

There has been an explosion in the volume of biology-related information that is available in online databases. But finding the right information can be challenging. Not only is this information spread over multiple sources, but often, it is hidden behind form interfaces of online databases. There are several ongoing efforts that aim to simplify the process of finding, integrating and exploring these data. However, existing approaches are not scalable, and require substantial manual input. Notable examples include the NCBI databases and the NAR database compilation. As an important step towards a scalable solution to this problem, we describe a new infrastructure that automates, to a large extent, the process of locating and organizing online databases. We show how this infrastructure can be used to automate the construction and maintenance of a Molecular Biology database collection. We also provide an evaluation which shows that the infrastructure is scalable and effective—it is able to efficiently locate and accurately identify the relevant online databases. [ABSTRACT FROM AUTHOR]

Methods to assess driving degradation due to driver distraction are currently discussed and defined by international standardization groups. A simulator experiment involving 17 participants was conducted to assess the reliability and relevance of one of these methods (Lane Change Test) to discriminate between secondary tasks. In addition to varying age groups, vehicles and secondary tasks, the protocol was also varied to assess the impact of the instruction occurrence and its possible conflict with primary task performance. Results show the limitations of the main parameter proposed by the method (lateral deviation) and question the reliability of the method in its current form. Additional indicators seem necessary to make sense of the respective impact of the varied conditions. Surprisingly, the impact of the instruction occurrence is very limited, apparently because individual strategies have more impact than situations differences. [ABSTRACT FROM AUTHOR]

This paper proposes the use of the stereographic projection within the realm of Computational Geometry for the design of tree-dimensional space structures arising from planar power diagrams. In order that such structures (to which we apply the term Chordal) can approximate a broad catalogue of quadrics, it will be necessary to formulate this projection under non-Euclidean metrics. [ABSTRACT FROM AUTHOR]

The rapid development of methods that select over/under expressed genes from microarray experiments have not yet matched the need for tools that identify informational profiles that differentiate between experimental conditions such as time, treatment and phenotype. Uncertainty arises when methods devoted to identify significantly expressed genes are evaluated: do all microarray analysis methods yield similar results from the same input dataset? do different microarray datasets require distinct analysis methods?. We performed a detailed evaluation of several microarray analysis methods, finding that none of these methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Consequently, we propose a procedure that, given certain user-defined preferences, generates an optimal suite of statistical methods. These solutions are optimal in the sense that they constitute partial ordered subsets of all possible method-associations bounded by both, the most specific and the most sensitive available solution. [ABSTRACT FROM AUTHOR]

The increased availability of biological databases containing representations of complex objects permits access to vast amounts of data. In spite of the recent renewed interest in knowledge-discovery techniques (or data mining), there is a dearth of data analysis methods intended to facilitate understanding of the represented objects and related systems by their most representative features and those relationship derived from these features (i.e., structural data). In this paper we propose a conceptual clustering methodology termed EMO-CC for Evolutionary Multi-Objective Conceptual Clustering that uses multi-objective and multi-modal optimization techniques based on Evolutionary Algorithms that uncover representative substructures from structural databases. Besides, EMO-CC provides annotations of the uncovered substructures, and based on them, applies an unsupervised classification approach to retrieve new members of previously discovered substructures. We apply EMO-CC to the Gene Ontology database to recover interesting substructures that describes problems from different points of view and use them to explain inmuno-inflammatory responses measured in terms of gene expression profiles derived from the analysis of longitudinal blood expression profiles of human volunteers treated with intravenous endotoxin compared to placebo. [ABSTRACT FROM AUTHOR]