Yu Y.,CAS Shanghai Institutes for Biological Sciences |
Yu Y.,University of Chinese Academy of Sciences |
Ping J.,Shanghai JiaoTong University |
Chen H.,Chinese National Human Genome Center at Shanghai |
And 7 more authors.
Genomics | Year: 2010
The human liver plays a vital role in meeting the body's metabolic needs and maintaining homeostasis. To address the molecular mechanisms of liver function, we integrated multiple gene expression datasets from microarray, MPSS, SAGE and EST platforms to generate a transcriptome atlas of the normal human liver. Our results show that 17396 genes are expressed in the human liver. 238 genes were identified as liver enrichment genes, involved in the functions of immune response and metabolic processes, from the MPSS and EST datasets. A comparative analysis of liver transcriptomes was performed in humans, mice and rats with microarray datasets shows that the expression profile of homologous genes remains significantly different between mouse/rat and human, suggesting a functional variance and regulation bias of genes expressed in the livers. The integrated liver transcriptome data should provide a valuable resource for the in-depth understanding of human liver biology and liver disease. © 2010.
Hu H.,Windber Research Institute |
Correll M.,InforSense |
Correll M.,Dana-Farber Cancer Institute |
Kvecher L.,Windber Research Institute |
And 14 more authors.
Journal of Biomedical Informatics | Year: 2011
The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types. © 2011 Elsevier Inc.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH-2007-2.4.2-6 | Award Amount: 3.43M | Year: 2009
The main purpose of the EVINCI-study is to test the impact of combined anatomo-functional non invasive cardiac imaging for detection and characterization of Ischemic Heart Disease (IHD). The EVINCI-study is a prospective clinical European multicenter trial performed in a cohort of 700 patients with suspected IHD. Patients with intermediate pre-test probability will undergo clinical and biohumoral characterization, including novel circulating markers of cardiovascular risk. They will be admitted to a non-invasive cardiac evaluation, consisting of anatomic imaging, by multislice computerized tomography, combined with functional tests among radionuclide, magnetic resonance and ultrasound imaging. Heart catheterization will be performed to validate non-invasive diagnosis and follow-up to assess outcome. The diagnostic accuracy of combined non-invasive anatomo-functional imaging will be tested against reference methods for diagnosing epicardial coronary lesions (coronary angiography), vessel wall atherosclerosis (intracoronary ultrasound) and impaired coronary flow reserve (intracoronary doppler/pressure wire). The individual profiles from anatomo-functional cardiac imaging and clinical-biohumoral data will be combined and tested against outcome. A cost-benefit analysis (including an estimate of procedural/radiological risks) of the new diagnostic work-up will also be performed. A relevant part of the EVINCI-study will be dedicated to the development, in cooperation with the industry, of an advanced informatics platform able to synthetically present to the end-user (patients, physicians, etc.) the integrated cardiological diagnostic profile of the individual patient as resulting from clinical-biohumoral and multi-imaging assessment. Overall results will be disseminated in cooperation with the European Society of Cardiology (ESC) and will guide the work of a dedicated ESC Commission which will release specific European Recommendations.
Azam N.,InforSense |
Ghanem M.,Imperial College London |
Kalaitzopoulos D.,InforSense |
Wolf A.,Fraunhofer Institute for Algorithms and Scientific Computing |
And 3 more authors.
International Journal of Ad Hoc and Ubiquitous Computing | Year: 2010
Enabling the seamless integration between applications executing on heterogeneous Grid middleware poses a number of challenges to both application scientists and middleware developers. We highlight some of these challenges in the context of designing and implementing DockFlow. DockFlow is a virtual screening environment integrating four Grid-based protein docking tools that execute on different Grid middleware technologies at different locations. We propose extensions that can be applied to any Grid-based workflow system to support the run-time interoperability between the available tools. The extensions are generic, and as an example we describe how they have been implemented in the InforSense workflow system. We also present experimental results that evaluate the tradeoffs between performance and usability of the proposed methods. © 2010 Inderscience Enterprises Ltd.
News Article | January 13, 2009
InforSense, a business intelligence software maker based in Cambridge, MA, and London, England, said today that founding CEO Yike Guo has stepped down, becoming chief technology officer, while David Bennett, the company’s former executive vice president of worldwide sales, has been promoted to CEO. At the same time, the company said it has secured an additional $5 million in financing from its existing investors, who include Elaia Partners, Fleming Family and Partners, Imperial Innovations Ltd, NPI Ventures, and Sitka Health Fund.
News Article | January 13, 2009
InforSense was founded in 1999 to commercialize pioneering, award-winning technology in the fields of High Performance Computing and Large Scale Data Mining developed at Imperial College, London. InforSense is a private company based in London, UK with additional offices in Cambridge, MA, USA and Shanghai, China.