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Computational modelling and Epidemiology

Section edited by Michael N. Liebman and Sabrina Molinaro

This section aims to allow rapid communication of methods and results that utilize computational modeling and epidemiologic approaches in translational medicine. The focus will be on population-based studies that extend towards more molecular level analysis. Submission of studies involving methods development is encouraged where actual application and results can be shown in the healthcare and life sciences domains. Among the approaches highlighted in this section are data-mining for health impact, data modeling, health record system design, applications of epidemiologic analysis, public policy evaluation, hypothesis evaluation and testing, risk assessment in healthcare and life sciences. These articles will describe the perspective on translational medicine where the approach and results reflect the computational modeling and epidemiologic aspects of this emerging area of research.

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  1. Human diseases frequently cause complications such as obesity-induced diabetes and share numbers of pathological conditions, such as inflammation, by dysfunctions of common functional modules, such as protein–...

    Authors: Hyojung Paik, Hyoung-Sam Heo, Hyo-jeong Ban and Seong Beom Cho

    Citation: Journal of Translational Medicine 2014 12:99

    Content type: Research

    Published on:

  2. The clinical prediction of survival is among the most challenging tasks because it refers to the process whereby the medical team assimilates clinical data using subjective methods. The purpose of this prospec...

    Authors: Liu Hui, Liu Qigui, Ren Sashuang, Liu Xiliang and Luan Guihong

    Citation: Journal of Translational Medicine 2014 12:78

    Content type: Research

    Published on:

  3. Each year in China, 30,000 babies are born with congenital hearing impairment. However, the molecular etiology of hearing impairment in the Yunnan Province population where more than 52 minorities live has not...

    Authors: Feng Xin, Yongyi Yuan, Xiaoming Deng, Mingyu Han, Guojian Wang, Jiandong Zhao, Xue Gao, Jun Liu, Fei Yu, Dongyi Han and Pu Dai

    Citation: Journal of Translational Medicine 2013 11:312

    Content type: Research

    Published on:

  4. Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug...

    Authors: Lena Brynne, Anders Bresell and Niclas Sjögren

    Citation: Journal of Translational Medicine 2013 11:250

    Content type: Methodology

    Published on:

  5. A new section of the Journal of Translational Medicine is being introduced to encourage rapid communication of methods and results that utilize computational modeling and epidemiologic approaches in translational...

    Authors: Michael N Liebman and Sabrina Molinaro

    Citation: Journal of Translational Medicine 2012 10:210

    Content type: Editorial

    Published on:

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