QIGTD：识别肺腺癌进化中的关键基因与张量分解
QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition
影响因子:6.10000
分区:生物学3区 / 数学与计算生物学3区
发表日期:2024 Sep 04
作者:
Bolin Chen, Jinlei Zhang, Ci Shao, Jun Bian, Ruiming Kang, Xuequn Shang
摘要
识别关键基因对于理解复杂疾病的发病机理很重要。传统研究通常比较正常样本和疾病样本之间的生物分子的变化或从单个静态生物分子网络中检测重要的顶点，该网络通常忽略不同疾病阶段之间发生的动态变化。然而，研究生物分子网络的时间变化并鉴定关键基因对于理解疾病的发生和发展至关重要。在本研究中提出了一种称为量化基因（QIGTD）重要性的新方法。它首先通过集成内部时间内网络信息和时间间网络信息来构建时间序列网络，该信息可以根据局部相似性在相邻阶段保持网络之间的连接。使用张量来描述该时间序列网络的连接，并提出了三阶张量分解方法来捕获每个网络快照的拓扑信息和整个网络的时间序列特征。 QIGTD也是一种无学习的方法，可以应用于少数样品的数据集。使用肺腺癌（LUAD）数据集评估了QiGTD的有效性和三种最先进的方法：T-degree，t-closesentes and t-closeness and t-cletness and T-betnesitess as as Benchmarks方法采用。数值实验结果表明，在精度和MAP的指标方面，QigTD优于这些方法。 Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.In conclusion, QIGTD effectively captures the temporal changes in gene networks and识别关键基因。它为研究生物网络中的时间动态提供了一种有价值的工具，并可以帮助理解诸如LUAD之类的疾病的潜在机制。
Abstract
Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases.A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples.The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD.