Semantic mashup of biomedical data

Integration and Querying of Heterogeneous Omics Semantic Annotations for Biomedical and Biomolecular Knowledge Discovery

Background:

Exploring various functional aspects of a biological cell system has been a focused research trend for last many decades. Biologists, scientists and researchers are continuously striving for unveiling the mysteries of these functional aspects to improve the health standards of life. For getting such understanding, astronomically growing, heterogeneous and geographically dispersed omics data needs to be critically analyzed. Currently, omics data is available in different types and formats through various data access interfaces. Applications which require offline and integrated data encounter a lot of data heterogeneity and global dispersion issues.

Objective:

For facilitating especially such applications, heterogeneous data must be collected, integrated and warehoused in such a loosely coupled way so that each molecular entity can computationally be understood independently or in association with other entities within or across the various cellular aspects.

Methods:

In this paper, we propose an omics data integration schema and its corresponding data warehouse system for integrating, warehousing and presenting heterogeneous and geographically dispersed omics entities according to the cellular functional aspects.

Results & Conclusion:

Such aspect-oriented data integration, warehousing and data access interfacing through graphical search, web services and application programing interfaces make our proposed integrated data schema and warehouse system better and useful than other contemporary ones.

Chapter 1: Biomedical knowledge integration

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

An informatics project and online "Knowledge Centre" supporting modern genotype-to-phenotype research

Explosive growth in the generation of genotype-to-phenotype (G2P) data necessitates a concerted effort to tackle the logistical and informatics challenges this presents. The GEN2PHEN Project represents one such effort, with a broad strategy of uniting disparate G2P resources into a hybrid centralized-federated network. This is achieved through a holistic strategy focussed on three overlapping areas: data input standards and pipelines through which to submit and collect data (data in); federated, independent, extendable, yet interoperable database platforms on which to store and curate widely diverse datasets (data storage); and data formats and mechanisms with which to exchange, combine, and extract data (data exchange and output). To fully leverage this data network, we have constructed the "G2P Knowledge Centre" (http://www.gen2phen.org). This central platform provides holistic searching of the G2P data domain allied with facilities for data annotation and user feedback, access to extensive G2P and informatics resources, and tools for constructing online working communities centered on the G2P domain. Through the efforts of GEN2PHEN, and through combining data with broader community-derived knowledge, the Knowledge Centre opens up exciting possibilities for organizing, integrating, sharing, and interpreting new waves of G2P data in a collaborative fashion.

A multi-lingual web service for drug side-effect data

In this paper, we describe a system that provides drug side-effect data for use as a component in service-oriented architectures. Our system uses "Web 2.0" techniques to collect data from a variety of public sources, and can provide its output in a variety of human languages (e.g. Spanish and Arabic). To demonstrate our tool's versatility and the ease with which it may be integrated into larger systems, we present several front-ends that use our system, including SMS ("text message"), "instant messenger", and iPhone interfaces. We enlisted a panel of Argentinean clinicians to review and rate the quality of our system's Spanish-language output in order to investigate whether freely-available general-purpose machine translation technology (Google's translation API) is adequate for consumer medical applications. Our raters found that Google's translation quality varied greatly among drugs, and we conclude that it is better used as a starting point than as a complete translation solution.

Translational informatics: enabling high-throughput research paradigms

A common thread throughout the clinical and translational research domains is the need to collect, manage, integrate, analyze, and disseminate large-scale, heterogeneous biomedical data sets. However, well-established and broadly adopted theoretical and practical frameworks and models intended to address such needs are conspicuously absent in the published literature or other reputable knowledge sources. Instead, the development and execution of multidisciplinary, clinical, or translational studies are significantly limited by the propagation of "silos" of both data and expertise. Motivated by this fundamental challenge, we report upon the current state and evolution of biomedical informatics as it pertains to the conduct of high-throughput clinical and translational research and will present both a conceptual and practical framework for the design and execution of informatics-enabled studies. The objective of presenting such findings and constructs is to provide the clinical and translational research community with a common frame of reference for discussing and expanding upon such models and methodologies.