网络中基于秩相关性的疾病和基因关联推理
Inferring Disease and Gene Set Associations with Rank Coherence in Networks
摘要：
验证在高通量基因组研究中鉴定的候选疾病基因的计算挑战是阐明候选基因集与疾病表型之间的关联。传统的基因集富集分析往往无法揭示疾病表型和基因集之间的关联，因为现有的致病基因的注释是不完整的。我们提出了一种基于网络的计算方法，称为rcNet，以发现基因集和疾病表型之间的关联。假设相干关联的基因之间的相关性排序的查询基因集，和疾病表型排序的相关性隐藏的目标疾病表型的查询基因集，我们制定了一个学习框架，最大限度地提高排名的一致性相对于已知的疾病表型基因的关联。一个有效的算法耦合岭回归和标签传播，并引入两个变种，以找到最佳的解决方案的框架。我们评估了rcNet算法和现有的基线方法，其中包括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。实验表明，与基线相比，rcNet算法实现了最佳的整体排名。为了进一步验证性能的可重复性，我们应用该算法来识别从最近的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靶疾病。在所有三个案例研究的许多情况下，算法将候选基因的靶疾病排在排名列表的顶部。rcNet算法可作为疾病和基因集关联分析的网络工具在http://compbio.cs.umn.edu/dgsa_rcNet上获得。
英文摘要：
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.
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。
