Anand Rajat, Chatterjee Samrat
Drug Discovery Research Centre, Translational Health Science and Technology Institute , Faridabad, India .
J Comput Biol. 2017 May;24(5):460-469. doi: 10.1089/cmb.2016.0090. Epub 2017 Mar 15.
Metabolic disorders such as obesity and diabetes are nowadays regarded as diseases affecting majority of population. These diseases develop gradually over time in an individual. Recently, systematic experiments tracking disease progression are conducted giving a high-throughput complex data. There is a pressing need for developing methods to analyze this complex data to capture the disease mechanism at molecular level. Diseases usually develop through perturbations of biological processes in an organism. In this study, we have tried to capture the interlinking between different biological processes that work together to regulate the disease phenotype. Here, we have considered a temporal microarray data from an experiment conducted to study obesity and diabetes in mice. We have analyzed the data to obtain perturbed biological processes and developed methods to establish link between these perturbed biological processes. We have derived a mathematical formula to score genes and identified a significant set of genes regulating such a complex process network. The methods developed in our study are also applicable to a broad array of data types.
肥胖和糖尿病等代谢紊乱如今被视为影响大多数人群的疾病。这些疾病在个体中会随着时间逐渐发展。最近，开展了追踪疾病进展的系统实验，产生了高通量的复杂数据。迫切需要开发分析此类复杂数据的方法，以便在分子水平上捕捉疾病机制。疾病通常通过生物体中生物过程的扰动而发展。在本研究中，我们试图捕捉共同作用以调节疾病表型的不同生物过程之间的相互联系。在此，我们考虑了一项为研究小鼠肥胖和糖尿病而进行的实验的时间微阵列数据。我们分析了数据以获得受扰动的生物过程，并开发了在这些受扰动的生物过程之间建立联系的方法。我们推导了一个对基因进行评分的数学公式，并鉴定出一组调节如此复杂过程网络的重要基因。我们研究中开发的方法也适用于广泛的数据类型。
