Elsworth Benjamin, Dawe Karen, Vincent Emma E, Langdon Ryan, Lynch Brigid M, Martin Richard M, Relton Caroline, Higgins Julian P T, Gaunt Tom R
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.
Int J Epidemiol. 2018 Jan 12;47(2):369-79. doi: 10.1093/ije/dyx251.
The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis.
Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts.
Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.
We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].
科学文献包含了来自不同领域关于潜在疾病机制的大量信息。然而，从已发表研究的数量和多样性来看，识别并优先考虑用于进一步分析评估的机制面临巨大挑战。将数据挖掘方法应用于文献有可能识别并优先考虑机制，以便进行更有针对性和详细的分析。
在此，我们介绍MELODI，一个能够识别任意两个生物医学概念之间机制性通路的文献挖掘平台。
两个案例研究展示了MELODI的潜在用途以及它如何生成可供进一步研究的假设。首先，对ETS相关基因ERG与前列腺癌的分析得出中间转录因子SP1，最近证实它与ERG存在物理相互作用。其次，研究胰腺癌一个新的潜在风险因素之间的关系，确定了可在体外研究的可能机制性见解。
我们展示了MELODI的可能应用，包括两个案例研究。MELODI已作为一个Python/Django网络应用程序实现，可在[www.melodi.biocompute.org.uk]免费使用。
