硕士学位论文：使用组合拍卖方法进行基因表达的排他双聚类
MSc Dissertation: Exclusive Row Biclustering for Gene Expression Using a Combinatorial Auction Approach
摘要：
在过去的十年中，大的微阵列数据的可用性导致了对双聚类方法越来越大的兴趣。已经提出了几种算法来识别基因和条件的子集，根据不同的相似性措施和不同的约束条件。本文研究了基因表达数据集的排他行双聚类问题，其中每一行只能是一个双聚类的成员，而列可以参与多个双聚类.这种类型的双聚类可能是足够的，例如，用于聚类癌症患者组，其中每个患者（行）预期仅携带单一类型的癌症，而每个癌症类型与多个（并且可能重叠的）基因（列）相关联。我们提出了一种新的方法来确定这些排他的双聚类通过现有的双聚类算法和组合拍卖技术相结合。我们设计了一种方法，用于调整阈值为我们的算法的基础上比较一个空模型的间隙统计方法的精神。我们证明了我们的方法在合成和真实世界的基因表达数据，并显示其在识别大跨度非重叠行子矩阵，同时考虑其独特的性质。Gap统计方法在我们所有的示例中成功地确定了适当的阈值。
英文摘要：
The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of genes and conditions according to different similarity measures and under varying constraints. In this paper we focus on the exclusive row biclustering problem for gene expression data sets, in which each row can only be a member of a single bicluster while columns can participate in multiple ones. This type of biclustering may be adequate, for example, for clustering groups of cancer patients where each patient (row) is expected to be carrying only a single type of cancer, while each cancer type is associated with multiple (and possibly overlapping) genes (columns). We present a novel method to identify these exclusive row biclusters through a combination of existing biclustering algorithms and combinatorial auction techniques. We devise an approach for tuning the threshold for our algorithm based on comparison to a null model in the spirit of the Gap statistic approach. We demonstrate our approach on both synthetic and real-world gene expression data and show its power in identifying large span non-overlapping rows sub matrices, while considering their unique nature. The Gap statistic approach succeeds in identifying appropriate thresholds in all our examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
