当前位置: X-MOL 学术IEEE/ACM Trans. Comput. Biol. Bioinform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2018-06-25 , DOI: 10.1109/tcbb.2018.2848904
Ye Liu , Michael K Ng , Stephen Wu

Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding. In many problems, different domains may have different cluster structures. Due to growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability.

中文翻译:

使用块签名图聚类的生物数据多域网络协会。

多域生物网络的关联和聚类在生物数据集成和理解中引起了很多关注。在许多问题中,不同的域可能具有不同的群集结构。由于来自不同来源的数据收集的增长,某些域可能与其他域紧密关联。一个关键的挑战是如何确定不同域之间的关联程度,以及如何通过数据集成来获得准确的聚类结果。在本文中,我们提出了一种使用块签名图聚类的多域网络关联无监督学习方法。特别地,通过一致性权重计算,所提出的算法通过为它们分配更大(或更小)的权重来自动(强或弱)识别彼此相关的域。这种方法不仅可以显着提高群集的准确性,而且可以了解多域网络的关联。在提出算法的每次迭代中,我们基于每个域的聚类结构更新一致性权重,然后利用不同的特征向量集在每个域中获得不同的聚类结构。在合成数据集和真实数据集(神经元活动数据和基因表达数据)上的实验结果都从经验上证明了该算法在聚类性能和域关联能力方面的有效性。然后利用不同的特征向量集在每个域中获得不同的簇结构。在合成数据集和真实数据集(神经元活动数据和基因表达数据)上的实验结果都从经验上证明了该算法在聚类性能和域关联能力方面的有效性。然后利用不同的特征向量集在每个域中获得不同的簇结构。在合成数据集和真实数据集(神经元活动数据和基因表达数据)上的实验结果都从经验上证明了该算法在聚类性能和域关联能力方面的有效性。
更新日期:2020-04-22
down
wechat
bug