Statistical methods for analysing multi-source microarray data and building gene regulatory networks

我将设计一种统计学习技术，它不会强制将一个基因精确地分配到一个类别。这项技术反映了一个基因可以属于两个或更多功能类别的生物学现实。因此，这项新技术将提高模型识别不同类型癌症中调控基因的能力；这些调节基因可以被新的抗癌药物靶向，从而产生更有效的治疗。我将使用来自多个来源的微阵列数据来模拟基因调控网络。这些网络将被用来识别调控集团——一组对细胞功能至关重要的基因。这将提高我们对诸如哮喘等衰弱性疾病的理解。

I will devise a statistical learning technique that does not force a gene to be assigned to exactly one category. This technique reflects the biological reality that a gene can belong to two or more functional categories. Therefore, the new technique will improve a model's ability to identify regulatory genes in different types of cancer; these regulatory genes can be targeted by new anti-cancer drugs resulting in a more effective treatment. I will model gene regulatory networks using microarray data from multiple sources. These networks will be used to identify regulatory cliques - a group of genes that are vital for a cellular function. This will improve our understanding of debilitating conditions such as asthma.