Algorithmiq Ltd, FI-00160 Helsinki, Finland.
Department of Mathematics and Statistics, Complex Systems Research Group, University of Turku, FI-20014, Turku, Finland.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae513.
Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end.
We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease.
The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.
疾病基因优先级方法根据给定疾病的一组种子基因，根据基因或蛋白质与疾病的相关性对其进行评分。这种评分可用于为许多疾病找到新的具有生物学相关性的基因或蛋白质。尽管基于经典随机游走的方法已被证明可产生有竞争力的结果，但尚未探索量子游走方法来实现这一目标。
我们提出了一种新的基于连续时间量子游走的疾病基因优先级算法，使用蛋白质-蛋白质相互作用（PPI）网络的邻接矩阵。我们通过将我们提出的量子游走方法与三种疾病集上的几种知名基因优先级方法进行比较，在七个不同的 PPI 网络上证明了我们方法的成功。为了比较这些方法，我们使用交叉验证并检查召回和平均精度值的倒数平均值。我们通过对冠心病预测基因进行富集分析进一步验证了我们的方法。
方法的数据和代码可在 https://github.com/markgolds/qdgp 上访问。
