Disease Gene Association finds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson’s disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer’s disease, further demonstrating its flexibility.

Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer’s disease.

疾病基因协会寻找与特定遗传疾病的表现有关的基因。我们提出一种实现多目标遗传算法的混合方法，其中输入由基于合并到一个复杂网络中的各种关系生物学证据类型的集中度度量组成。研究了多个目标设置和参数，包括开发新的交易方法，基于经销商的安全分频器。与以前的技术和流行的已知数据库相比，在乳腺癌和帕金森氏病方面取得了成功的结果。此外，新开发的方法还成功应用于阿尔茨海默氏病，进一步证明了其灵活性。

在所有这三个案例研究中，无论是在单个目标参数设置中还是在多目标环境中结合使用时，最短的基于路径的测度应力和中间性都能产生最强的结果。当应用于阿尔茨海默氏病时，新的交叉技术达到了最佳效果。