Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)

Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

Semantic web data warehousing for caGrid

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG® Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

Integration of Biological Sources: Exploring the Case of Protein Homology

Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration

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A Survey of Semantic Integration Approaches in Bioinformatics

Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community