A data aggregation framework for cancer subtype discovery

S. N. Nagabhushan, T. Ahn, M. Srikanth, T. Park, Ajit S. Bopardikar, R. Narayanan
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Abstract

Personalized genomic medicine aims to revolutionize healthcare by applying our growing understanding of the molecular basis of disease for effective diagnosis and personalized therapy. Computational research in this arena has major challenges such as handling large volume of highly heterogeneous data sets. To extract knowledge, researchers must integrate data from several sources and efficiently query these large and diverse data sets. This presents daunting informatics challenges such as suitable data representation for computational inference (knowledge representation), linking heterogeneous data sets (data integration) and keeping track of the source of the data to be aggregated. Many of these challenges can be categorized as data integration problems. In this paper, we present relevant methodologies from the field of data integration as potential solution for such challenges encountered by computational biologist while handling diversified data. The work presented in the paper represents the first crucial step towards identifying cancer biomarkers leading to cancer pathways signatures and personalized medicine.
癌症亚型发现的数据聚合框架
个性化基因组医学旨在通过应用我们对疾病分子基础的不断增长的理解来进行有效的诊断和个性化治疗,从而彻底改变医疗保健。该领域的计算研究面临着重大挑战,例如处理大量高度异构的数据集。为了提取知识,研究人员必须整合多个来源的数据,并有效地查询这些庞大而多样的数据集。这就提出了令人生畏的信息学挑战,例如适合计算推理的数据表示(知识表示)、链接异构数据集(数据集成)以及跟踪要聚合的数据源。这些挑战中的许多都可以归类为数据集成问题。在本文中,我们提出了数据集成领域的相关方法,作为计算生物学家在处理多样化数据时遇到的这些挑战的潜在解决方案。论文中提出的工作代表了确定癌症生物标志物的关键第一步,从而导致癌症途径签名和个性化医疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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