In fact, any data source that can be extracted into relevant instance pairs is applicable. Integrative neuroscience involves the integration and analysis of diverse types of neuroscience data involving many different experimental techniques. As a result, a substantial number of these studies have shown interest in utilizing computational techniques, commonly known as gene prioritization methods. Other technologies, such as natural language processing and text mining, which are becoming increasingly important to the Semantic Web, are also discussed. More than ever, life science researchers depend on information from multiple sources. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. After first presenting papers on the foundations of semantic e-science, including papers on scientific knowledge acquisition, data integration, and workflow, this volume looks at the state of the art in each of the above-mentioned disciplines, presenting research on semantic web applications in the life, earth, materials, and social sciences.
The experimental methods for identification of these genes are usually time-consuming and expensive. Categorization of genes based on endophenotype associations by this method will be useful for further hypothesis generation leading to clinical and translational analysis. In this context this book seeks to offer students, researchers, and professionals a glimpse of the technology, its capabilities and the reach of its current implementation in the Life Sciences. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. We propose a flexible approach that empowers multi-source information reconciliation for quality gene prioritization that augments the complementary nature of various learning sources so as to utilize the maximum information of aggregated data. In complex disorders, collaborative role of several genes accounts for the multitude of symptoms and the discovery of molecular mechanisms requires proper understanding of pertinent genes. However, such a research area does not yet exist in a coherent form.
This special issue is intended to give an introduction of the state-of-the-art of Semantic Web technologies and describe how such technologies would be used to build the e-Science infrastructure for biomedical communities. Cheung is also an invited expert to the Semantic Web Health Care and Life Science Interest Group launched by the World Wide Web Consortium. This ability will be achieved through the widespread acceptance and application of standards for naming, representing, describing and accessing biological information. The Semantic Web offers a powerful new strategy for consolidating both text and structured data into a comprehensive collections and views. It urgently requires the development of a multidisciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches. This paper reviews the need for, and explores advantages of as well as challenges with these novel Internet information tools as illustrated with examples from the biomedical community. Keywordsrecognition-biophysical filters-contact maps-Bayesian procedure-Markov chain-protein folding The Semantic Web is now a research discipline in its own right and commercial interest in applications of Semantic Web technologies is strong.
Following the success of SeS2006, this workshop aimed at providing an interdisciplinary forum for researchers from both artificial intelligence including the semantic technology, and general science communities including the life science community. Scientists reading the book will see that the complex needs of biology and medicine are being addressed. Both experimental and computational approaches have been exploited in recent studies to explore disease-susceptible genes. However, to realize this potential, scientists and information technologists must forge new models of cooperation, and new thinking must go into the funding and dissemination of this next generation of scientific tools on the Web. This paper proposes a novel method to remove outliers based on density estimation and it has been applied to real-world traffic data. Authors: Huajun Chen, Yuxin Mao, Xiaoqing Zheng, Meng Cui, Yi Feng, Shuiguang Deng, Aining Yin, Chunying Zhou, Jinming Tang, Xiaohong Jiang and Zhaohui Wu.
Quinlan Matthew: Applying Semantic Web Technologies to Drug Safety Determination. Areas covered in this review: We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. This special issue is intended to give an introduction of the state-of-the-art of Semantic Web technologies and describe how such technologies would be used to build the e-Science infrastru cture for biomedical communities. In this paper, a novel technique called debugging ontology mappings is presented. In this paper, we introduce a semantic relation verification method based on both domain ontology and domain publications.
The conventional approaches to outlier removal either assume that the data follow a certain known distribution or deal with the data that are from a single distribution, resulting in a reduced credibility of the data processed. At protein level plant-fungal interaction upsurge the need to understand protein homeostasis and molecular adaptation of building blocks of cell to manifest natural selection for the host. Semantic Web: Revolutionizing Knowledge Discovery in Life Sciences is divided into six parts that cover the topics of: knowledge integration, knowledge representation, knowledge visualization, utilization of formal knowledge representations, and access to distributed knowledge. Different areas of computer science e. In the past few years, many researches have been conducted on identifying and prioritizing disease-related genes with the goal of achieving significant improvements in treatment and drug discovery.
Furthermore, to obtain improved results for a particular disease of interest, HybridRanker incorporates data from diseases with similar symptoms and also from its comorbid diseases. With the ongoing rapid increase in both volume and diversity of 'omic' data genomics, transcriptomics, proteomics, and others , the development and adoption of data standards is of paramount importance to realize the promise of systems biology. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. The experimental results reveal that the ontology debugging technique is promising, and it can improve the quality of mapping result. Particularly, the survey covers three major application categories including: i semantic integration and open data linking; ii semantic web service and scientific collaboration and iii semantic data mining and integrative network analysis. Scott Marshall, Marco Roos, Edgar Meij, Sophia Katrenko, Willem Robert van Hage, and Pieter W. The World Wide Web has revolutionized how researchers from various disciplines collaborate over long distances.
Although the Internet has revolutionized the way our society thinks about information, the traditional text-based framework of the scientific article remains largely unchanged. Yimin Wang is an associate information consultant in Lilly Singapore Centre for Drug Discovery. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. This approach may also be useful in other complex neurological and psychiatric disorders with a strong genetic component. More importantly, some errors and warnings can be repaired automatically or can be presented to users with revising suggestions.
By comparison with the conventional approach, the experimental results indicate that the proposed algorithm is capable of detecting and removing outliers effectively for the data that may follow different unknown distributions, and the processed data retain the original and significant characteristics possessed by the system. BioDash: a Semantic Web dashboard for drug development. An emerging generation of World Wide Web technology, known as the Semantic Web, offers tremendous potential for collaborative and interdisciplinary science. Scientific articles are tailored to present information in human-readable aliquots. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. This data will increasingly be distributed across many heterogeneous databases that are web-accessible. The next challenge is to integrate this vast and ever-growing body of information with academic journals and other media.
He received his PhD degree in Computer Science from the University of Connecticut. In any case, given that various sorts of heterogeneous sources are possibly significant for quality gene prioritization, every source bearing data not conveyed by another, we assert that a perfect strategy ought to give approaches to observe among them in a genuine integrative style that catches the degree of each, instead of utilizing a straightforward mix of sources. But our method is not limited to this field. Academic journals alone cannot capture the findings of modern genome-scale inquiry. The problem is that once you have gotten your nifty new product, the semantic e science cheung kei hoi chen huajun wang yimin gets a brief glance, maybe a once over, but it often tends to get discarded or lost with the original packaging.