VPH NoE

The Virtual Physiological Human Network of Excellence (VPH NoE) proposal has been designed with 'service to the community' of VPH researchers as its primary purpose. Its aims range from the development of a VPH ToolKit and associated infrastructural resources, through integration of models and data across the various relevant levels of physiological structure and functional organisation, to VPH community building and support. The VPH NoE aims to foster the development of new and sustainable educational, training and career structures for those involved in VPH related science, technology and medicine, and will lay the foundations for a future Virtual Physiological Human Institute.

The VPH NoE constitutes a leading group of universities, institutes and organisations who will, by integrating their experience and ongoing activities in VPH research, promote the creation of an environment that actively supports and nurtures interdisciplinary research, education, training and strategic development. The VPH NoE will lead the coordination of diverse activities within the VPH initiative to deliver: new environments for predictive, patient-specific, evidence-based, more effective and safer healthcare; improved semantic interoperability of biomedical information and contribution to a common health information infrastructure; facile, on-demand access to distributed European computational infrastructure to support clinical decision making; and increased European multidisciplinary research excellence in biomedical informatics and molecular medicine by fostering closer cooperation between ICT, medical device, medical imaging, pharmaceutical and biotech companies.

The VPH NoE will connect the diverse VPH projects, including not only those funded as part of the VPH initiative but also those of previous EC frameworks and national funding schemes, together with industry, healthcare providers, and international organisations, thereby ensuring that these impacts will be realised.

For further information, please visit:
http://www.vph-noe.eu

Project co-ordinator:
University College London (UCL)

Partners:

  • Institut Municipal d'Assistència Sanitària (IMAS)
  • Centre National de la Recherche Scientifique (CNRS)
  • The University of Nottingham
  • Europäisches Laboratorium für Molekularbiologie EMBL
  • The Chancellor, Master and Scholars of the University of Oxford
  • GEIE ERCIM
  • The University of Sheffield
  • Universitat Pompeu Fabra
  • Université Libre de Bruxelles
  • Institut National de Recherche en Informatique et en Automatique
  • The University of Auckland
  • Karolinska Institutet

Timetable: from 06/2008 - to 11/2012

Total cost: € 9.649.516

EC funding: € 7.999.367

Programme Acronym: FP7-ICT

Subprogramme Area: Virtual physiological human

Contract type: Networks of Excellence


Related news article:

Most Popular Now

500 Patient Images per Second Shared thr…

The image exchange portal, widely known in the NHS as the IEP, is now being used to share as many as 500 images each second - including x-rays, CT, MRI...

Is Your Marketing Effective for an NHS C…

How can you make sure you get the right message across to an NHS chief information officer, or chief nursing information officer? Replay this webinar with Professor Natasha Phillips, former...

We could Soon Use AI to Detect Brain Tum…

A new paper in Biology Methods and Protocols, published by Oxford University Press, shows that scientists can train artificial intelligence (AI) models to distinguish brain tumors from healthy tissue. AI...

Welcome Evo, Generative AI for the Genom…

Brian Hie runs the Laboratory of Evolutionary Design at Stanford, where he works at the crossroads of artificial intelligence and biology. Not long ago, Hie pondered a provocative question: If...

Telehealth Significantly Boosts Treatmen…

New research reveals a dramatic improvement in diagnosing and curing people living with hepatitis C in rural communities using both telemedicine and support from peers with lived experience in drug...

AI can Predict Study Results Better than…

Large language models, a type of AI that analyses text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a new study led by UCL...

Using AI to Treat Infections more Accura…

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections...

Research Study Shows the Cost-Effectiven…

Earlier research showed that primary care clinicians using AI-ECG tools identified more unknown cases of a weak heart pump, also called low ejection fraction, than without AI. New study findings...

New Guidance for Ensuring AI Safety in C…

As artificial intelligence (AI) becomes more prevalent in health care, organizations and clinicians must take steps to ensure its safe implementation and use in real-world clinical settings, according to an...

Remote Telemedicine Tool Found Highly Ac…

Collecting images of suspicious-looking skin growths and sending them off-site for specialists to analyze is as accurate in identifying skin cancers as having a dermatologist examine them in person, a...

Philips Aims to Advance Cardiac MRI Tech…

Royal Philips (NYSE: PHG, AEX: PHIA) and Mayo Clinic announced a research collaboration aimed at advancing MRI for cardiac applications. Through this investigation, Philips and Mayo Clinic will look to...

Deep Learning Model Accurately Diagnoses…

Using just one inhalation lung CT scan, a deep learning model can accurately diagnose and stage chronic obstructive pulmonary disease (COPD), according to a study published today in Radiology: Cardiothoracic...