标题： 基于网络中排名一致性推断疾病和基因集关联 显示英文标题

标题： Inferring Disease and Gene Set Associations with Rank Coherence in Networks

摘要： 一种计算挑战是验证在高通量基因组研究中识别的候选疾病基因，以阐明候选基因集合与疾病表型之间的关联。传统的基因集富集分析常常无法揭示疾病表型与注释较差的基因集合之间的关联，因为现有疾病致病基因的注释不完整。我们提出了一种基于网络的计算方法，称为rcNet，以发现基因集合与疾病表型之间的关联。假设由查询基因集合的相关性排序的基因和由查询基因集合的隐藏目标疾病表型的相关性排序的疾病表型之间存在一致的关联，我们构建了一个学习框架，该框架最大化相对于已知疾病表型-基因关联的排名一致性。引入了一种将岭回归与标签传播耦合的高效算法以及两种变体，以找到框架的最佳解决方案。我们使用留一交叉验证和预测OMIM中最近发现的疾病-基因关联的任务来评估rcNet算法和现有的基线方法。实验表明，与基线方法相比，rcNet算法取得了最佳的整体排名。为了进一步验证性能的可重复性，我们将这些算法应用于识别从最近的GWAS、DNA拷贝数变异分析和基因表达谱研究中获得的新候选疾病基因的目标疾病。在所有三个案例研究中，算法在许多情况下将候选基因的目标疾病排在排名列表的顶部。rcNet算法作为网络工具，可用于疾病和基因集合关联分析，网址为http://compbio.cs.umn.edu/dgsa_rcNet。 显示英文摘要

摘要： A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation, and two variants are introduced to find the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered disease-gene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of GWAS, DNA copy number variation analysis, and gene expression profiling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. The rcNet algorithms are available as a webtool for disease and gene set association analysis at http://compbio.cs.umn.edu/dgsa_rcNet.