Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, United States of America.
PLoS One. 2023 Mar 16;18(3):e0283004. doi: 10.1371/journal.pone.0283004. eCollection 2023.
Oncogenetic graphical models are crucial for understanding cancer progression by analyzing the accumulation of genetic events. These models are used to identify statistical dependencies and temporal order of genetic events, which helps design targeted therapies. However, existing algorithms do not account for temporal differences between samples in oncogenetic analysis. This paper introduces Timed Hazard Networks (TimedHN), a new statistical model that uses temporal differences to improve accuracy and reliability. TimedHN models the accumulation process as a continuous-time Markov chain and includes an efficient gradient computation algorithm for optimization. Our simulation experiments demonstrate that TimedHN outperforms current state-of-the-art graph reconstruction methods. We also compare TimedHN with existing methods on a luminal breast cancer dataset, highlighting its potential utility. The Matlab implementation and data are available at https://github.com/puar-playground/TimedHN.
癌发生图形模型通过分析遗传事件的积累对于理解癌症进展至关重要。这些模型用于识别遗传事件的统计依赖性和时间顺序，这有助于设计靶向治疗。然而，现有的算法在癌发生分析中没有考虑到样本之间的时间差异。本文介绍了定时危险网络（TimedHN），这是一种新的统计模型，它利用时间差异来提高准确性和可靠性。TimedHN 将积累过程建模为连续时间马尔可夫链，并包括用于优化的高效梯度计算算法。我们的仿真实验表明，TimedHN 优于当前最先进的图形重建方法。我们还在乳癌数据集上比较了 TimedHN 与现有方法，突出了它的潜在效用。Matlab 实现和数据可在 https://github.com/puar-playground/TimedHN 上获得。
