Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.
School of Mathematics Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.
BMC Bioinformatics. 2019 Sep 9;20(1):462. doi: 10.1186/s12859-019-3056-2.
Determining the association between tumor sample and the gene is demanding because it requires a high cost for conducting genetic experiments. Thus, the discovered association between tumor sample and gene further requires clinical verification and validation. This entire mechanism is time-consuming and expensive. Due to this issue, predicting the association between tumor samples and genes remain a challenge in biomedicine.
Here we present, a computational model based on a heat diffusion algorithm which can predict the association between tumor samples and genes. We proposed a 2-layered graph. In the first layer, we constructed a graph of tumor samples and genes where these two types of nodes are connected by "hasGene" relationship. In the second layer, the gene nodes are connected by "interaction" relationship. We applied the heat diffusion algorithms in nine different variants of genetic interaction networks extracted from STRING and BioGRID database. The heat diffusion algorithm predicted the links between tumor samples and genes with mean AUC-ROC score of 0.84. This score is obtained by using weighted genetic interactions of fusion or co-occurrence channels from the STRING database. For the unweighted genetic interaction from the BioGRID database, the algorithms predict the links with an AUC-ROC score of 0.74.
We demonstrate that the gene-gene interaction scores could improve the predictive power of the heat diffusion model to predict the links between tumor samples and genes. We showed the efficient runtime of the heat diffusion algorithm in various genetic interaction network. We statistically validated our prediction quality of the links between tumor samples and genes.
由于进行遗传实验的成本很高，因此确定肿瘤样本与基因之间的关联具有挑战性。因此，进一步需要临床验证和确认所发现的肿瘤样本与基因之间的关联。整个机制既耗时又昂贵。由于这个问题，预测肿瘤样本和基因之间的关联仍然是生物医学中的一个挑战。
在这里，我们提出了一种基于热扩散算法的计算模型，该模型可以预测肿瘤样本和基因之间的关联。我们提出了一个双层图。在第一层中，我们构建了一个肿瘤样本和基因的图，其中这两种类型的节点通过“hasGene”关系连接。在第二层中，基因节点通过“相互作用”关系连接。我们在从 STRING 和 BioGRID 数据库中提取的 9 种不同的遗传相互作用网络变体中应用了热扩散算法。热扩散算法预测了肿瘤样本和基因之间的链接，AUC-ROC 评分的平均值为 0.84。这一分数是通过使用来自 STRING 数据库的融合或共发生通道的加权遗传相互作用获得的。对于来自 BioGRID 数据库的非加权遗传相互作用，该算法预测链接的 AUC-ROC 得分为 0.74。
我们证明了基因-基因相互作用分数可以提高热扩散模型预测肿瘤样本和基因之间链接的预测能力。我们展示了热扩散算法在各种遗传相互作用网络中的高效运行时间。我们对肿瘤样本和基因之间链接的预测质量进行了统计验证。
