IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul-Aug;19(4):2219-2230. doi: 10.1109/TCBB.2021.3069441. Epub 2022 Aug 8.
Tracking the dynamic modules (modules change over time) during cancer progression is essential for studying cancer pathogenesis, diagnosis, and therapy. However, current algorithms only focus on detecting dynamic modules from temporal cancer networks without integrating the heterogeneous genomic data, thereby resulting in undesirable performance. To attack this issue, we propose a novel algorithm (aka TANMF) to detect dynamic modules in cancer temporal attributed networks, which integrates the temporal networks and gene attributes. To obtain the dynamic modules, the temporality and gene attributed are incorporated into an overall objective function, which transforms the dynamic module detection into an optimization problem. TANMF jointly decomposes the snapshots at two subsequent time steps to obtain the latent features of dynamic modules, where the attributes are fused via regulations. Furthermore, the L constraint is imposed to improve the robustness. Experimental results demonstrate that TANMF is more accurate than state-of-the-art methods in terms of accuracy. By applying TANMF to breast cancer data, the obtained dynamic modules are more enriched by the known pathways and associated with patients' survival time. The proposed model and algorithm provide an effective way for the integrative analysis of heterogeneous omics.
跟踪癌症进展过程中的动态模块（模块随时间变化）对于研究癌症发病机制、诊断和治疗至关重要。然而，当前的算法仅侧重于从时间癌症网络中检测动态模块，而没有整合异构的基因组数据，从而导致不理想的性能。为了解决这个问题，我们提出了一种新的算法（即 TANMF）来检测癌症时间属性网络中的动态模块，该算法将时间网络和基因属性集成在一起。为了获得动态模块，将时间性和基因属性合并到一个总体目标函数中，将动态模块检测转化为优化问题。TANMF 联合分解两个后续时间步的快照以获得动态模块的潜在特征，其中通过规则融合属性。此外，施加 L 约束以提高稳健性。实验结果表明，TANMF 在准确性方面优于最先进的方法。通过将 TANMF 应用于乳腺癌数据，获得的动态模块通过已知途径得到了更丰富的补充，并与患者的生存时间相关。所提出的模型和算法为异质组学的综合分析提供了一种有效的方法。
