Ma Xiaoke, Sun Penggang, Qin Guimin
IEEE/ACM Trans Comput Biol Bioinform. 2017 Oct 10. doi: 10.1109/TCBB.2017.2761339.
Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the Specific Modules in Multiple Networks (SMMN), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.
必须确定多个网络中特定疾病状态的模块，以揭示疾病潜在的分子机制。当前的算法存在局限性，如准确性低和对网络数量高度敏感，因为这些算法通过分离模块的特异性和模块化来在多个网络中发现特定疾病状态的模块。为了克服这些局限性，我们将特定疾病状态的模块表征为一组基因，其在相应网络中的连接性强，而在其他网络中的连接性弱；这种策略可以准确描绘特定疾病状态模块的拓扑结构。然后，我们将特定疾病状态模块发现问题转化为多个网络中的聚类问题。我们开发了一种针对多网络中特定模块（SMMN）的高效启发式算法，该算法通过考虑多个网络来发现特定疾病状态的模块。通过使用人工网络，我们证明SMMN优于现有方法。在乳腺癌网络中，SMMN发现的特定阶段模块在预测癌症阶段方面比其他技术获得的模块更具判别力。在泛癌网络中，癌症特异性模块更有可能与患者的生存时间相关，这对癌症治疗至关重要。
