Sheikh Kashif Raffat

Semantic Grid for Biomedical Ontologies

ABSTRACT The biomedical ontologies contain the complex distributed heterogeneous data, to analyze and process this data is the big challenge for biomedical communities. The common goal of biomedical communities is to annotate this data. These problems generated a need to use the services of grid on semantic web. Semantic Grid is the integration of Grid with the Semantic web, which will play the vital role in future web. The semantic grid architecture provides semantic and knowledge support. In this paper we discuss two biomedical ontologies, Biological Viruses Community Ontology (BVCO) and the most mature Gene Ontology (GO). General Terms Biomedical data, Future Web. Keywords Biomedical Ontology, Grid Computing, Semantic Grid, Semantic Web. 1. INTRODUCTION Biological sciences are facing the exponential growth of observational, experimental and theoretical data scattered in different laboratories and hospitals. These are distributed heterogeneous datasets because of their source (Imaging device, Sequencer etc.). These laboratories and hospitals are generally not capable to archive, process and analyze these terabyte of dataset. The bioinformatics research community is erg to analyze these distributed heterogeneous dataset in order to extract useful information and knowledge. But these resources which are generating vast amount of biomedical data are widely distributed, highly heterogeneous, may follows heterogeneous protocols and made by different venders so applying appropriate computational technique in this kind of heterogeneous environment is not an easy task. Now a day Grid Computing is playing an important role in obtaining, comparing and analyzing distributed heterogeneous scientific data. Foster [1] defines the Grid concept as “the controlled and coordinated resource sharing and problem solving in dynamic, multi institutional virtual organizations”. This sharing of resource, ranging from simple file transfer to complex and collaborative problem solving, is accomplished with in controlled and well-defined conditions and policies. The dynamic grouping of individuals, groups, or organization that defined the conditions and rules for sharing are called virtual organization (VO) [2]. The grid computing resources include computing power, data storage, hardware instruments, on-demand software and applications. In this context, the real problems involved with resource sharing are resource discovery, event correlation, authentication, authorization and access mechanism. These problems become proportionately more complicated when the grid computing solution is introduced as a solution for utility computing, where industrial applications and resources become available as sharable [3]. According to Tom Gruber [4], “Ontology defines a set of representational primitives that are typically classes, attributes and relationships”. Ontology can be viewed as a controlled vocabulary of well-defined terms with specified relationships between them, capable of interpretation by both human and computers. Many tools are also available to develop ontology like OBO–Edit (mainly used for Biological Ontologies), Protege (developed by the Stanford University, USA) and TODE [5] (developed by the National University of Computer and Emerging Sciences, Karachi, Pakistan). Biomedical research community sequencing more and more genomes day by day and they highly needed processing and analyzing with appropriate algorithms. The integration of grid computing and ontology can play vital role in for biomedical communities [6, 7]. In this paper we are presenting an idea for exploration of huge amount of biomedical datasets by using semantic grids.

BCIO: Brain Computer Interface Ontology

Brain Computer Interface (BCI) enable paralyze peoples to interact and control their environment by defining the direct communication between human feelings (brain) and technological aspects (external device). The feature extraction and translation of commands in BCI are the critical tasks, which are the key of BCI system. Using the semantic method will enhance the both of these features. Nowadays ontology for representing knowledge is becoming more popular in researchers to describe, share and integrate their scientific data. If the information, algorithms and results are stored in the form of ontological content, it will provide the efficient way to use and reuse the data related to BCI and will help to BCI researchers for standardization. Adding semantic in BCI will also improve the efficiency of current BCI system. General Terms Human Factor, Brain Computer Interface.