An automated framework for hypotheses generation using literature.
An automated framework for hypotheses generation using literature.
复制标题
使用文献生成的假设生成的自动框架。
DOI：
10.1186/1756-0381-5-13
复制
发表时间：
2012-08-29
期刊：
影响因子：
4.5
通讯作者：
Faisal FE
中科院分区：
文献类型：
--
作者：
Abedi V;Zand R;Yeasin M;Faisal FE
在生物医学中，探索性研究和假设生成通常从研究现有文献开始，以确定一组因素及其与疾病、表型或生物过程的关联。当许多科学家计划提出新的假说或研究一种生物现象时，他们被关于一种疾病的大量文献淹没了。对于初级调查人员来说，情况更糟，他们经常发现很难提出新的假设，或者更重要的是，如果他们的假设与现有文献一致，就很难证实。这是一项艰巨的任务，既要与如此多的出版物并驾齐驱，又要记住直接和间接联系的所有组合。幸运的是，在生物医学研究中使用文献挖掘和知识发现工具的趋势越来越大。然而，投入在疾病研究上的巨大努力和资源，与收获已发表的知识的微不足道的努力之间，仍然有很大的差距。拟议的假设生成框架(HGF)在感兴趣的实体之间找到了“清晰的语义关联”--这是朝着弥合这些差距迈出的一步。拟议的HGF与天鹅有类似的最终目标，但本质上更具整体性，并使用可扩展和高效的疾病-疾病相互作用计算模型来设计和实施。映射本体与潜在语义分析的集成对于捕获特定领域的直接和间接“清晰”关联，以及对实体作出断言(例如，疾病X与一组因素Z相关联)至关重要。使用两种疾病进行了初步研究。对计算出的“联系”和“断言”与精选的专家知识进行了比较分析，以验证结果。据观察，HGF能够捕捉“清晰”的直接和间接关联，并按需提供知识发现。建议的框架在生成新的假设以确定与疾病相关的因素方面是快速、高效和健壮的。目前正在开发一个全面整合的网络服务应用程序，以广泛传播人类安全论坛。一项由领域专家和相关研究人员进行的大规模研究正在进行中，以验证HGF计算出的关联和断言。
In bio-medicine, exploratory studies and hypothesis generation often begin with researching existing literature to identify a set of factors and their association with diseases, phenotypes, or biological processes. Many scientists are overwhelmed by the sheer volume of literature on a disease when they plan to generate a new hypothesis or study a biological phenomenon. The situation is even worse for junior investigators who often find it difficult to formulate new hypotheses or, more importantly, corroborate if their hypothesis is consistent with existing literature. It is a daunting task to be abreast with so much being published and also remember all combinations of direct and indirect associations. Fortunately there is a growing trend of using literature mining and knowledge discovery tools in biomedical research. However, there is still a large gap between the huge amount of effort and resources invested in disease research and the little effort in harvesting the published knowledge. The proposed hypothesis generation framework (HGF) finds “crisp semantic associations” among entities of interest - that is a step towards bridging such gaps. The proposed HGF shares similar end goals like the SWAN but are more holistic in nature and was designed and implemented using scalable and efficient computational models of disease-disease interaction. The integration of mapping ontologies with latent semantic analysis is critical in capturing domain specific direct and indirect “crisp” associations, and making assertions about entities (such as disease X is associated with a set of factors Z). Pilot studies were performed using two diseases. A comparative analysis of the computed “associations” and “assertions” with curated expert knowledge was performed to validate the results. It was observed that the HGF is able to capture “crisp” direct and indirect associations, and provide knowledge discovery on demand. The proposed framework is fast, efficient, and robust in generating new hypotheses to identify factors associated with a disease. A full integrated Web service application is being developed for wide dissemination of the HGF. A large-scale study by the domain experts and associated researchers is underway to validate the associations and assertions computed by the HGF.
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