It is a big challenge to develop efficient models for identifying personalized drug targets (PDTs) from high-dimensional personalized genomic profile of individual patients. Recent structural network control principles have introduced a new approach to discover PDTs by selecting an optimal set of driver genes in personalized gene interaction network (PGIN). However, most of current methods only focus on controlling the system through a minimum driver-node set and ignore the existence of multiple candidate driver-node sets for therapeutic drug target identification in PGIN. Therefore, this paper proposed multi-objective optimization-based structural network control principles (MONCP) by considering minimum driver nodes and maximum prior-known drug-target information. To solve MONCP, a discrete multi-objective optimization problem is formulated with many constrained variables, and a novel evolutionary optimization model called LSCV-MCEA was developed by adapting a multi-tasking framework and a rankings-based fitness function method. With genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, the effectiveness of LSCV-MCEA was validated. The experimental results indicated that compared with other advanced methods, LSCV-MCEA can more effectively identify PDTs with the highest Area Under the Curve score for predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-MCEA can more effectively solve MONCP than other evolutionary optimization methods in terms of algorithm convergence and diversity. Particularly, LSCV-MCEA can efficiently detect disease signals for individual patients with BRCA cancer. The study results show that multi-objective optimization can solve structural network control principles effectively and offer a new perspective for understanding tumor heterogeneity in cancer precision medicine.

翻译：从个体患者的高维个性化基因组图谱中识别个性化药物靶点（PDTs）是一项重大挑战。近期结构网络控制原理通过选择个性化基因互作网络（PGIN）中的最优驱动基因集，为发现PDTs提供了新方法。然而，现有方法大多仅聚焦于通过最小驱动节点集控制系统，忽视了PGIN中用于治疗药物靶点识别的多个候选驱动节点集的存在。为此，本文提出基于多目标优化的结构网络控制原理（MONCP），综合考虑最小驱动节点与最大先验药物-靶点信息。为求解MONCP，本文构建了包含多个约束变量的离散多目标优化问题，并开发了一种新型进化优化模型LSCV-MCEA，该模型通过适配多任务框架与基于排序的适应度函数方法实现。利用癌症基因组图谱数据库中乳腺癌或肺癌患者的基因组数据，验证了LSCV-MCEA的有效性。实验结果表明，与其他先进方法相比，LSCV-MCEA在识别PDTs时能获得预测临床注释组合药物的最高曲线下面积得分。同时，在算法收敛性与多样性方面，LSCV-MCEA比其他进化优化方法更有效地求解MONCP。特别是，LSCV-MCEA能高效检测BRCA癌症个体患者的疾病信号。研究结果表明，多目标优化能有效求解结构网络控制原理，并为理解癌症精准医学中的肿瘤异质性提供了新视角。