Briefings in Bioinformatics - recent issues

Editorial

Dubitzky, W. — 2009-06-07

Approaches to neuroscience data integration

2009-06-07

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

Genome assembly reborn: recent computational challenges

Pop, M. — 2009-06-07

Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data; however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain.

Computational biology for cardiovascular biomarker discovery

Azuaje, F., Devaux, Y., Wagner, D. — 2009-06-07

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using ‘omic’ information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of ‘omic’ data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of ‘omic’ approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

FINDSITE: a combined evolution/structure-based approach to protein function prediction

Skolnick, J., Brylinski, M. — 2009-06-07

A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the ~50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used.

Biological knowledge management: the emerging role of the Semantic Web technologies

Antezana, E., Kuiper, M., Mironov, V. — 2009-06-07

New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.

Computational methods for discovering gene networks from expression data

Lee, W.-P., Tzou, W.-S. — 2009-06-07

Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists’ viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.

Computational systems biology of the cell cycle

Csikasz-Nagy, A. — 2009-06-07

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

Flux balance analysis of biological systems: applications and challenges

Raman, K., Chandra, N. — 2009-06-07

Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis (FBA), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, FBA is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. FBA has been used to analyse genome-scale reconstructions of several organisms; it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of FBA as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from FBA, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.

The virtual cell--a candidate co-ordinator for 'middle-out' modelling of biological systems

Walker, D. C., Southgate, J. — 2009-06-07

Understanding the functioning of biological systems depends on tackling complexity spanning spatial scales from genome to organ to whole organism. The basic unit of life, the cell, acts to co-ordinate information received across these scales and processes the myriad of signals to produce an integrated cellular response. Cells interact with and respond to other cells through direct or indirect contact, resulting in emergent structure and function of tissues and organs. Systems biology has traditionally used either a ‘top-down’ or ‘bottom-up’ approach. However, neither approach takes account of heterogeneity or ‘noise’, which is an inherent feature of cellular behaviour and may have significant impact on system level behaviour. We review existing approaches to modelling that use cellular automata or agent-based methodologies, where individual cells are represented as equivalent virtual entities governed by simple rules. These paradigms allow a direct one-to-one mapping between real and virtual cells that can be exploited in terms of acquiring parameters from experimental systems, or for model validation. Such models are inherently extensible and can be integrated with other modelling modalities (e.g. partial or ordinary differential equations) to model multi-scale phenomena. Alternatively, hierarchical agent models may be used to explore the functions of biological systems across temporal and spatial scales. This review examines individual-based models and the application of the paradigm to explore multi-scale phenomena in biology. In so doing, it demonstrates how cellular-based models have begun to play an important role in the development of ‘middle-out’ models, but with considerable potential for future development.

Exploring autonomy through computational biomodelling

Palfreyman, N. — 2009-06-07

The question of whether living organisms possess autonomy of action is tied up with the nature of causal efficacy. Yet the nature of organisms is such that they frequently defy conventional causal language. Did the fig wasp select the fig, or vice versa? Is this an epithelial cell because of its genetic structure, or because it develops within the epithelium? The intimate coupling of biological levels of organisation leads developmental systems theory to deconstruct the biological organism into a life-cycle process which constitutes itself from the resources available within a complete developmental system. This radical proposal necessarily raises questions regarding the ontological status of organisms: Does an organism possess existence distinguishable from its molecular composition and social environment? The ambiguity of biological causality makes such questions difficult to answer or even formulate, and computational biology has an important role to play in operationalising the language in which they are framed. In this article we review the role played by computational biomodels in shedding light on the ontological status of organisms. These models are drawn from backgrounds ranging from molecular kinetics to niche construction, and all attempt to trace biological processes to a causal, and therefore existent, source. We conclude that computational biomodelling plays a fertile role in furnishing a proof of concept for conjectures in the philosophy of biology, and suggests the need for a process-based ontology of biological systems.

Domain mobility in proteins: functional and evolutionary implications

Basu, M. K., Poliakov, E., Rogozin, I. B. — 2009-04-21

A substantial fraction of eukaryotic proteins contains multiple domains, some of which show a tendency to occur in diverse domain architectures and can be considered mobile (or ‘promiscuous’). These promiscuous domains are typically involved in protein–protein interactions and play crucial roles in interaction networks, particularly those contributing to signal transduction. They also play a major role in creating diversity of protein domain architecture in the proteome. It is now apparent that promiscuity is a volatile and relatively fast-changing feature in evolution, and that only a few domains retain their promiscuity status throughout evolution. Many such domains attained their promiscuity status independently in different lineages. Only recently, we have begun to understand the diversity of protein domain architectures and the role the promiscuous domains play in evolution of this diversity. However, many of the biological mechanisms of protein domain mobility remain shrouded in mystery. In this review, we discuss our present understanding of protein domain promiscuity, its evolution and its role in cellular function.

A survey of available tools and web servers for analysis of protein-protein interactions and interfaces

Tuncbag, N., Kar, G., Keskin, O., Gursoy, A., Nussinov, R. — 2009-04-21

The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins; to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem; what are the tool advantages and drawbacks; and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.

Progress and challenges in predicting protein-protein interaction sites

2009-04-21

The identification of protein–protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

2D molecular graphics: a flattened world of chemistry and biology

Zhou, P., Shang, Z. — 2009-04-21

Molecular graphics provides an intuitive way for representation, modeling and analysis of complex chemical and biological systems. It is now widely used in the theoretical chemistry, structural biology, molecular modeling and drug design communities. Traditional molecular graphics techniques mainly dedicate to showing molecular architectures at three-dimensional (3D) level. However, in some occasions the two-dimensional (2D) representation of molecular configurations, profiles, behaviors and interactions may be more readily acceptable for audiences, especially when we need to describe abstract information in a straightforward way or to present numerous data in schematic diagrams. In recent years, 2D representation methods/tools have been developed rapidly for various purposes, ranging from the aesthetic depiction of atomic arrangement for small organic molecules to schematic layout of complicated nonbonding network across the biomolecular binding interfaces, and have received considerable interest in the fields of chemistry, biology and medicine. In this article we first propose the term of 2D molecular graphics to cover the spectrum of 2D representing chemical and biological systems, we also give a comprehensive review on the methods, tools and applications of 2D molecular graphics.

Probes containing runs of guanines provide insights into the biophysics and bioinformatics of Affymetrix GeneChips

Langdon, W. B., Upton, G. J. G., Harrison, A. P. — 2009-04-21

The reliable interpretation of Affymetrix GeneChip data is a multi-faceted problem. The interplay between biophysics, bioinformatics and mining of GeneChip surveys is leading to new insights into how best to analyse the data. Many of the molecular processes occurring on the surfaces of GeneChips result from the high surface density of probes. Interactions between neighbouring adjacent probes affect their rate and strength of hybridization to targets. Competing targets may hybridize to the same probe, and targets may partially bind to more than one probe. The formation of these partial hybrids results in a number of probes not reaching thermodynamic equilibrium during hybridization. Moreover, some targets fold up, or cross-hybridize to other targets. Furthermore, probes may fold and can undergo chemical saturation. There are also sequence-dependent differences in the rates of target desorption during the washing stage. Improvements in the mappings between probe sequence and biological databases are leading to more accurate gene expression profiles. Moreover, algorithms that combine the intensities of multiple probes into single measures of expression are increasingly dependent upon models of the hybridization processes occurring on GeneChips. The large repositories of GeneChip data can be searched for systematic effects across many experiments. This data mining has led to the discovery of a family of thousands of probes, which show correlated expression across thousands of GeneChip experiments. These probes contain runs of guanines, suggesting that G-quadruplexes are able to form on GeneChips. We discuss the impact of these structures on the interpretation of data from GeneChip experiments.

Taming the complexity of biological pathways through parallel computing

Ballarini, P., Guido, R., Mazza, T., Prandi, D. — 2009-04-21

Biological systems are characterised by a large number of interacting entities whose dynamics is described by a number of reaction equations. Mathematical methods for modelling biological systems are mostly based on a centralised solution approach: the modelled system is described as a whole and the solution technique, normally the integration of a system of ordinary differential equations (ODEs) or the simulation of a stochastic model, is commonly computed in a centralised fashion. In recent times, research efforts moved towards the definition of parallel/distributed algorithms as a means to tackle the complexity of biological models analysis. In this article, we present a survey on the progresses of such parallelisation efforts describing the most promising results so far obtained.

Potential Bias in GO::TermFinder

Flight, R. M., Wentzell, P. D. — 2009-04-21

The increased need for multiple statistical comparisons under conditions of non-independence in bioinformatics applications, such as DNA microarray data analysis, has led to the development of alternatives to the conventional Bonferroni correction for adjusting P-values. The use of the false discovery rate (FDR), in particular, has grown considerably. However, the calculation of the FDR frequently depends on drawing random samples from a population, and inappropriate sampling will result in a bias in the calculated FDR. In this work, we demonstrate a bias due to incorrect random sampling in the widely used GO::TermFinder package. Both T² and permutation tests are used to confirm the bias for a test set of data, which leads to an overestimation of the FDR of about 10%. A simple fix to the random sampling method is proposed to remove the bias.

A pitfall of wiki solution for biological databases

Arita, M. — 2009-04-21

Not a few biologists tend to consider wiki as a solution to manage and reorganize data by a community. However, in its basic functionality, wiki lacks a measure to check data consistency and is not suitable for a database. To circumvent this pitfall, installation of page dependency through in-line page searches is necessary. We also introduce two existing approaches that support in-line queries.

A roadmap of clustering algorithms: finding a match for a biomedical application

Andreopoulos, B., An, A., Wang, X., Schroeder, M. — 2009-04-21

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Lancashire, L. J., Lemetre, C., Ball, G. R. — 2009-04-21

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

2009-04-21

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at <igrid-ext.cryst.bbk.ac.uk/immunogrid>.

Introduction to Computational Genomics: A Case Studies Approach Nello Cristianini and Matthew W. Hahn.

Ahituv, N. — 2009-04-21

Semantic Web for Health Care and Life Sciences: a review of the state of the art

Cheung, K.-H., Prud'hommeaux, E., Wang, Y., Stephens, S. — 2009-03-20

Moby and Moby 2: Creatures of the Deep (Web)

Vandervalk, B. P., McCarthy, E. L., Wilkinson, M. D. — 2009-03-20

Facile and meaningful integration of data from disparate resources is the ‘holy grail’ of bioinformatics. Some resources have begun to address this problem by providing their data using Semantic Web standards, specifically the Resource Description Framework (RDF) and the Web Ontology Language (OWL). Unfortunately, adoption of Semantic Web standards has been slow overall, and even in cases where the standards are being utilized, interconnectivity between resources is rare. In response, we have seen the emergence of centralized ‘semantic warehouses’ that collect public data from third parties, integrate it, translate it into OWL/RDF and provide it to the community as a unified and queryable resource. One limitation of the warehouse approach is that queries are confined to the resources that have been selected for inclusion. A related problem, perhaps of greater concern, is that the majority of bioinformatics data exists in the ‘Deep Web’—that is, the data does not exist until an application or analytical tool is invoked, and therefore does not have a predictable Web address. The inability to utilize Uniform Resource Identifiers (URIs) to address this data is a barrier to its accessibility via URI-centric Semantic Web technologies. Here we examine ‘The State of the Union’ for the adoption of Semantic Web standards in the health care and life sciences domain by key bioinformatics resources, explore the nature and connectivity of several community-driven semantic warehousing projects, and report on our own progress with the CardioSHARE/Moby-2 project, which aims to make the resources of the Deep Web transparently accessible through SPARQL queries.

Building biomedical web communities using a semantically aware content management system

Das, S., Girard, L., Green, T., Weitzman, L., Lewis-Bowen, A., Clark, T. — 2009-03-20

Web-based biomedical communities are becoming an increasingly popular vehicle for sharing information amongst researchers and are fast gaining an online presence. However, information organization and exchange in such communities is usually unstructured, rendering interoperability between communities difficult. Furthermore, specialized software to create such communities at low cost—targeted at the specific common information requirements of biomedical researchers—has been largely lacking. At the same time, a growing number of biological knowledge bases and biomedical resources are being structured for the Semantic Web. Several groups are creating reference ontologies for the biomedical domain, actively publishing controlled vocabularies and making data available in Resource Description Framework (RDF) language. We have developed the Science Collaboration Framework (SCF) as a reusable platform for advanced structured online collaboration in biomedical research that leverages these ontologies and RDF resources. SCF supports structured ‘Web 2.0’ style community discourse amongst researchers, makes heterogeneous data resources available to the collaborating scientist, captures the semantics of the relationship among the resources and structures discourse around the resources. The first instance of the SCF framework is being used to create an open-access online community for stem cell research—StemBook (http://www.stembook.org). We believe that such a framework is required to achieve optimal productivity and leveraging of resources in interdisciplinary scientific research. We expect it to be particularly beneficial in highly interdisciplinary areas, such as neurodegenerative disease and neurorepair research, as well as having broad utility across the natural sciences.

Linked data and provenance in biological data webs

Zhao, J., Miles, A., Klyne, G., Shotton, D. — 2009-03-20

The Web is now being used as a platform for publishing and linking life science data. The Web's linking architecture can be exploited to join heterogeneous data from multiple sources. However, as data are frequently being updated in a decentralized environment, provenance information becomes critical to providing reliable and trustworthy services to scientists. This article presents design patterns for representing and querying provenance information relating to mapping links between heterogeneous data from sources in the domain of functional genomics. We illustrate the use of named resource description framework (RDF) graphs at different levels of granularity to make provenance assertions about linked data, and demonstrate that these assertions are sufficient to support requirements including data currency, integrity, evidential support and historical queries.

Towards pharmacogenomics knowledge discovery with the semantic web

Dumontier, M., Villanueva-Rosales, N. — 2009-03-20

Pharmacogenomics aims to understand pharmacological response with respect to genetic variation. Essential to the delivery of better health care is the use of pharmacogenomics knowledge to answer questions about therapeutic, pharmacological or genetic aspects. Several XML markup languages have been developed to capture pharmacogenomic and related information so as to facilitate data sharing. However, recent advances in semantic web technologies have presented exciting new opportunities for pharmacogenomics knowledge discovery by representing the information with machine understandable semantics. Progress in this area is illustrated with reference to the personalized medicine project that aims to facilitate pharmacogenomics knowledge discovery through intuitive knowledge capture and sophisticated question answering using automated reasoning over expressive ontologies.

Scaling the walls of discovery: using semantic metadata for integrative problem solving

Manning, M., Aggarwal, A., Gao, K., Tucker-Kellogg, G. — 2009-03-20

Current data integration approaches by bioinformaticians frequently involve extracting data from a wide variety of public and private data repositories, each with a unique vocabulary and schema, via scripts. These separate data sets must then be normalized through the tedious and lengthy process of resolving naming differences and collecting information into a single view. Attempts to consolidate such diverse data using data warehouses or federated queries add significant complexity and have shown limitations in flexibility. The alternative of complete semantic integration of data requires a massive, sustained effort in mapping data types and maintaining ontologies. We focused instead on creating a data architecture that leverages semantic mapping of experimental metadata, to support the rapid prototyping of scientific discovery applications with the twin goals of reducing architectural complexity while still leveraging semantic technologies to provide flexibility, efficiency and more fully characterized data relationships. A metadata ontology was developed to describe our discovery process. A metadata repository was then created by mapping metadata from existing data sources into this ontology, generating RDF triples to describe the entities. Finally an interface to the repository was designed which provided not only search and browse capabilities but complex query templates that aggregate data from both RDF and RDBMS sources. We describe how this approach (i) allows scientists to discover and link relevant data across diverse data sources and (ii) provides a platform for development of integrative informatics applications.

Semantic web for integrated network analysis in biomedicine

Chen, H., Ding, L., Wu, Z., Yu, T., Dhanapalan, L., Chen, J. Y. — 2009-03-20

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb–drug interactions analysis.

Life sciences on the Semantic Web: the Neurocommons and beyond

Ruttenberg, A., Rees, J. A., Samwald, M., Marshall, M. S. — 2009-03-20

Translational research, the effort to couple the results of basic research to clinical applications, depends on the ability to effectively answer questions using information that spans multiple disciplines. The Semantic Web, with its emphasis on combining information using standard representation languages, access to that information via standard web protocols, and technologies to leverage computation, such as in the form of inference and distributable query, offers a social and technological basis for assembling, integrating and making available biomedical knowledge at Web scale. In this article, we discuss the use of Semantic Web technology for assembling and querying biomedical knowledge from multiple sources and disciplines. We present the Neurocommons prototype knowledge base, a demonstration intended to show the feasibility and benefits of using these technologies. The prototype knowledge base can be used to experiment with and assess the scalability of current tools and methods for creating such a resource, and to elicit issues that will need to be addressed in order to expand the scope and use of it. We demonstrate the utility of the knowledge base by reviewing a few example queries that provide answers to precise questions relevant to the understanding of disease. All components of the knowledge base are freely available at http://neurocommons.org/, enabling readers to reconstruct the knowledge base and experiment with this new technology.