Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores.
Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores.
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DOI：
10.1371/journal.pone.0049634
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Madeira SC
中科院分区：
文献类型：
--
作者：
Gonçalves JP;Francisco AP;Moreau Y;Madeira SC
疾病基因优先排序旨在表明基因对疾病易感性的潜在影响。通常通过关联犯罪计划来完成，有前途的候选者根据其与已知疾病基因的相关性进行排序。基于网络的方法已经成功地利用了这个概念，将基因或蛋白质的相互作用捕获到一个分数中。尽管如此，大多数当前的方法至少存在以下一些局限性：（1）网络仅包含精心策划的物理相互作用，导致基因组覆盖率和密度较差，并且偏向于特定来源； (2) 分数侧重于疾病基因周围受限邻域内的邻接（直接链接）或最直接路径（最短路径），忽略潜在的信息丰富的间接路径； （3）全局聚类被广泛应用于以无监督的方式划分网络，对先验知识的重视程度较低； (4) 置信度权重及其对边缘区分和排名可靠性的贡献常常被忽视。我们假设与图上的局部聚类相关的基于网络的优先级排序并考虑集成异构源的加权基因关联网络的完整拓扑应该克服上述挑战。我们将这种策略称为交互策略。我们进行了交叉验证测试，以评估网络来源、替代路径包含和置信权重对 29 种疾病的推定基因优先顺序的影响。热扩散排序被证明是总体上最好的优先级排序方法，主要在单源网络上增加了与邻域的差距和最短路径得分。在大多数方法中，异质关联始终提供优于单一源数据的性能。关于置信权重贡献的结果尚无定论。最后，使用来自 STRING 数据库的最佳交互策略、热扩散排名和关联来对帕金森病基因进行优先排序。这种方法有效地恢复了已知基因，并发现了可能与疾病致病机制相关的有趣候选基因。
Disease gene prioritization aims to suggest potential implications of genes in disease susceptibility. Often accomplished in a guilt-by-association scheme, promising candidates are sorted according to their relatedness to known disease genes. Network-based methods have been successfully exploiting this concept by capturing the interaction of genes or proteins into a score. Nonetheless, most current approaches yield at least some of the following limitations: (1) networks comprise only curated physical interactions leading to poor genome coverage and density, and bias toward a particular source; (2) scores focus on adjacencies (direct links) or the most direct paths (shortest paths) within a constrained neighborhood around the disease genes, ignoring potentially informative indirect paths; (3) global clustering is widely applied to partition the network in an unsupervised manner, attributing little importance to prior knowledge; (4) confidence weights and their contribution to edge differentiation and ranking reliability are often disregarded. We hypothesize that network-based prioritization related to local clustering on graphs and considering full topology of weighted gene association networks integrating heterogeneous sources should overcome the above challenges. We term such a strategy Interactogeneous. We conducted cross-validation tests to assess the impact of network sources, alternative path inclusion and confidence weights on the prioritization of putative genes for 29 diseases. Heat diffusion ranking proved the best prioritization method overall, increasing the gap to neighborhood and shortest paths scores mostly on single source networks. Heterogeneous associations consistently delivered superior performance over single source data across the majority of methods. Results on the contribution of confidence weights were inconclusive. Finally, the best Interactogeneous strategy, heat diffusion ranking and associations from the STRING database, was used to prioritize genes for Parkinson’s disease. This method effectively recovered known genes and uncovered interesting candidates which could be linked to pathogenic mechanisms of the disease.
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影响因子：
5.8
作者：
Goncalves, Joana P.;Francisco, Alexandre P.;Madeira, Sara C.
通讯作者：
Madeira, Sara C.
影响因子：
14.9
作者：
Finn RD;Mistry J;Tate J;Coggill P;Heger A;Pollington JE;Gavin OL;Gunasekaran P;Ceric G;Forslund K;Holm L;Sonnhammer EL;Eddy SR;Bateman A
通讯作者：
Bateman A
影响因子：
3.9
作者：
Bademci, Gueney;Edwards, Todd L.;Wang, Liyong
通讯作者：
Wang, Liyong
影响因子：
4
作者：
Albert, R
通讯作者：
Albert, R
DOI：
10.1007/978-1-59745-398-1_20
发表时间：
2008-01-01
期刊：
FUNCTIONAL PROTEOMICS: METHODS AND PROTOCOLS
影响因子：
--
作者：
Chatr-aryamontri, Andrew;Zanzoni, Andreas;Cesareni, Gianni
通讯作者：
Cesareni, Gianni