Information Sciences最新文献_第5页

MO-DMLHM: Multi-objective dynamic hypergraph modeling for cross-layer community detection in organizational networks 组织网络跨层社区检测的多目标动态超图建模

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-29 DOI: 10.1016/j.ins.2026.123170

You-hong Li , Jian-Qiang Wang , Le Gao , Tian-Yu Wang , Hao-Ming Mo

Community detection in organizational networks is vital for optimizing team structures, yet existing methods face critical challenges: Static models ignore temporal dynamics, dynamic single-layer approaches overlook cross-layer interactions, and multi-objective frameworks often optimize goals in isolation, leading to suboptimal real-world performance. We propose the Multi-Objective Dynamic Multi-Layer Hypergraph Modeling Framework (MO-DMLHM), integrating three innovations: (1) Adaptive Dynamic Hypergraph Modeling with dual-scale decay and adaptive time windowing to capture spatiotemporal dynamics; (2) Four-Dimensional Multi-Objective Optimization balancing modularity, cross-layer consistency, stability, and efficiency via Pareto-optimal NSGA-III; (3) Hybrid Encoding Evolutionary Algorithm jointly optimizing hyperedge activation and node membership through spectral clustering-guided mutation and betweenness centrality-driven crossover. Experiments on diverse organizational networks show MO-DMLHM outperforms state-of-the-art methods in detection accuracy, cross-layer alignment, and stability, reducing coordination costs by nearly 40%. Ablation studies confirm the necessity of dynamic modeling, multi-objective optimization, and hybrid encoding. MO-DMLHM resolves structural-community decoupling in dynamic multi-layer systems, advancing complex network analysis and enabling adaptive governance in organizations, with extensions to smart cities, biological networks, and financial risk management.

组织网络中的社区检测对于优化团队结构至关重要，但现有方法面临着严峻的挑战：静态模型忽略了时间动态，动态单层方法忽略了跨层交互，多目标框架通常孤立地优化目标，导致现实世界的次优性能。本文提出了多目标动态多层超图建模框架（MO-DMLHM），该框架集成了以下三个创新：(1)采用双尺度衰减和自适应时间窗的自适应动态超图建模来捕捉时空动态；(2)利用Pareto-optimal NSGA-III实现模块化、跨层一致性、稳定性和效率的四维多目标优化；(3)混合编码进化算法通过谱聚类引导的突变和中间度中心性驱动的交叉，共同优化超边缘激活和节点隶属。在不同组织网络上的实验表明，MO-DMLHM在检测精度、跨层对齐和稳定性方面优于最先进的方法，将协调成本降低了近40%。消融研究证实了动态建模、多目标优化和混合编码的必要性。MO-DMLHM解决了动态多层系统中的结构-社区解耦问题，推进了复杂网络分析，实现了组织中的自适应治理，并扩展到智慧城市、生物网络和金融风险管理。

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引用次数: 0

ESP-based prescribed performance formation control for vehicle platoon systems with input saturation: A fully actuated system approach 基于esp的输入饱和车辆排系统的规定性能编队控制：一种全驱动系统方法

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-28 DOI: 10.1016/j.ins.2026.123161

Meilin Lei , Zhechen Zhu , Yingnan Pan , Yan Lei

Prescribed performance control (PPC) plays a vital role in vehicle platoon systems by ensuring their safe and stable operation, and its effectiveness is commonly limited by spacing policies, initial conditions, and input saturation. This paper investigates an improved PPC strategy under these influences within the framework of the fully actuated system approach. Firstly, an improved exponential spacing policy (IESP) incorporating the leader’s velocity information is proposed to mitigate the effects of velocity fluctuations on inter-vehicle spacing. Subsequently, a novel shifting function is designed such that the spacing error converges inside the prescribed region within the settling time, thus eliminating the dependence on the initial spacing error. The time-varying convergence boundary of the proposed performance function improves the adaptability of the system to sudden changes in the road environment. In addition, the input saturation problem is addressed using the hyperbolic tangent function. Finally, all the signals of the system are proven to be semi-globally ultimately uniformly bounded, ensuring the internal stability, string stability, and traffic flow stability. The effectiveness of the proposed strategy is verified via simulation results.

规定性能控制（PPC）是车辆排系统安全稳定运行的保证，其有效性通常受到间距策略、初始条件和输入饱和等因素的限制。本文在全驱动系统方法的框架下，研究了一种改进的PPC策略。首先，提出了一种包含前车速度信息的改进指数间隔策略（IESP），以减轻速度波动对车辆间距的影响。随后，设计了一种新颖的位移函数，使间距误差在沉降时间内收敛于规定区域内，从而消除了对初始间距误差的依赖。所提性能函数的时变收敛边界提高了系统对道路环境突变的适应性。此外，使用双曲正切函数解决了输入饱和问题。最后证明了系统的所有信号是半全局最终一致有界的，保证了系统的内部稳定性、串稳定性和交通流稳定性。仿真结果验证了该策略的有效性。

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引用次数: 0

Global context modeling for image super-resolution transformer 图像超分辨率转换器的全局上下文建模

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123135

Dongsheng Ruan , Yuan Zheng , Lide Mu , Ao Ran , Lei Pan , Mingfeng Jiang , Chengjin Yu , Nenggan Zheng , Huafeng Liu

Window-based Transformers have achieved remarkable results in image super-resolution (SR). State-of-the-art SR models generally employ a window self-attention mechanism combined with a multi-layer perceptron (MLP) to effectively capture long-range dependencies. However, the window design and the MLP’s deficiency in capturing spatial dependencies restrict their capacity to utilize global contextual information in images. This paper aims to address this limitation by introducing global context modeling. Specifically, we propose a general global context-injected framework for window self-attention. Within this framework, we develop a new instantiation with a novel global context-injected (GCI) module, which allows each window to take advantage of the contextual information from other windows. The GCI module is lightweight and can be easily integrated into existing window-based Transformers, improving performance with negligible increases in parameters and computational costs. Furthermore, we introduce a window self-attention (WSA) to vision state space (VSS) flow to further enhance the ability for global context modeling. We incorporate our advancements into popular SR models, such as SwinIR and SRFormer, creating enhanced versions. Extensive experiments on three representative SR tasks demonstrate the effectiveness of our methods, showing substantial performance improvements over their vanilla counterparts. Notably, our GCI-MSRformer outperforms current state-of-the-art models like MambaIR.

基于窗口的变形器在图像超分辨率（SR）方面取得了显著的成绩。最先进的SR模型通常采用窗口自注意机制结合多层感知器（MLP）来有效捕获远程依赖关系。然而，窗口设计和MLP在捕获空间依赖性方面的不足限制了它们利用图像中全局上下文信息的能力。本文旨在通过引入全局上下文建模来解决这一限制。具体来说，我们提出了一个通用的全局上下文注入框架，用于窗口自关注。在这个框架中，我们开发了一个新的实例化，使用一个新的全局上下文注入（GCI）模块，它允许每个窗口利用来自其他窗口的上下文信息。GCI模块重量轻，可以很容易地集成到现有的基于窗口的变压器中，在参数和计算成本几乎可以忽略不计的情况下提高性能。此外，在视觉状态空间流中引入窗口自关注（WSA），进一步增强了全局上下文建模的能力。我们将我们的进步纳入流行的SR模型，如SwinIR和SRFormer，创建增强版本。在三个具有代表性的SR任务上进行的大量实验证明了我们的方法的有效性，显示出与普通任务相比有实质性的性能改进。值得注意的是，我们的gci - msformer比MambaIR等当前最先进的机型性能更好。

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引用次数: 0

Multi-attribute group consensus decision-making with two-stage trust risk adjustment 基于两阶段信任风险调整的多属性群体共识决策

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123162

Peide Liu , Yurong Qian , Ran Dang , Fei Teng , Peng Wang

As decision-making problems continue to expand, group decision-making (GDM) has seen growing interest in the application of social trust networks (STN). The trust capacity stems of a decision maker (DM) from both self-trust and external social support. Misalignment between the two may lead to trust crises or overconfidence, affecting decision outcomes. Under a two-dimensional linguistic (2DL) environment, a consensus method for multi-attribute group decision-making (MAGDM) that combines internal and external trust mechanisms is presented in this paper. First, DMs’ self-trust is assessed through subjective judgment in the 2DL setting, and then compared with social trust support from the STN in a two-stage trust risk evaluation to align individual competence with external expectations. Next, individual opinions are optimized during the consensus process while managing trust risk. In attribute assignment, individual weights are determined through the interaction between DMs and STN, and attribute weights are calculated using information entropy. To better capture DMs’ psychological behavior, such as regret and hesitation during comparisons, a new MAGDM ranking method integrating regret theory and the SIR method is proposed to improve decision reliability. Lastly, through an illustrative application in an actual data element market context and comparative analysis, the effectiveness of the proposed method is demonstrated, offering actionable insights to support decisions pertaining to data factor market development.

随着决策问题的不断扩大，群体决策（GDM）对社会信任网络（STN）的应用越来越感兴趣。决策者的信任能力来源于自我信任和外部社会支持。两者之间的不一致可能导致信任危机或过度自信，从而影响决策结果。在二维语言环境下，提出了一种结合内外信任机制的多属性群体决策共识方法。首先，通过主观判断在2DL环境下评估dm的自我信任，然后在两阶段信任风险评估中将其与STN的社会信任支持进行比较，以使个人能力与外部期望保持一致。其次，在共识过程中对个体意见进行优化，同时管理信任风险。在属性分配中，通过dm和STN之间的相互作用确定单个权重，并利用信息熵计算属性权重。为了更好地捕捉决策主体在比较过程中的后悔、犹豫等心理行为，提出了一种将后悔理论与SIR方法相结合的MAGDM排序方法，以提高决策的可靠性。最后，通过在实际数据要素市场背景下的说明性应用和比较分析，证明了所提出方法的有效性，为支持有关数据要素市场发展的决策提供了可操作的见解。

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引用次数: 0

SSDSNet: Dual-stage self-supervised network for low-light image enhancement 用于弱光图像增强的双级自监督网络

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-02-05 DOI: 10.1016/j.ins.2026.123191

Zhuo-Ming Du, Qian Yu, Fei-long Han

Low-light image enhancement is challenging due to scarce paired data and the sensitivity of supervised methods to illumination variations. We propose SSDSNet, a self-supervised dual-stage network that operates without normal-light references. In the first stage, SSDSNet integrates classical enhancement techniques to recover structural details and suppress noise, producing an intermediate HDR-like representation. The second stage refines color and contrast through complementary self-supervised objectives. Extensive experiments on real-world low-light images demonstrate that SSDSNet achieves state-of-the-art PSNR and SSIM, validating its robustness, generalization, and effectiveness without labeled data.

由于缺乏成对数据和监督方法对光照变化的敏感性，弱光图像增强具有挑战性。我们提出了SSDSNet，这是一个自我监督的双阶段网络，在没有正常光参考的情况下运行。在第一阶段，SSDSNet集成了经典增强技术来恢复结构细节和抑制噪声，产生类似hdr的中间表示。第二阶段通过互补的自我监督目标来完善颜色和对比度。在真实世界低光图像上的大量实验表明，SSDSNet实现了最先进的PSNR和SSIM，验证了其鲁棒性、泛化性和有效性，无需标记数据。

引用次数: 0

Space tree-based graph continuous cellular automaton for unit commitment and economic dispatch optimization 基于空间树型图连续元胞自动机的机组投入与经济调度优化

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-02-03 DOI: 10.1016/j.ins.2026.123199

Siyi Zhou , Li’ao Chen , Xingyu Liang , Min Xia , Shi Liang , Jun Liu , Jiayue Hu

Unit Commitment (UC) and Economic Dispatch (ED) are core issues in the power system. In this paper we try to solve both problems jointly: UC-ED problem is a large-scale mixed-integer linear programming (MILP) problem. Algorithms based on mathematical optimization cannot solve large-scale problems, models based on heuristic algorithms tend to fall into local optima, and methods based on deep learning generally cannot directly handle constraint violations. To address UC-ED problem, a new framework: Space tree-based graph continuous cellular automaton (ST-GCCA) has been proposed. It extracts fused features through an autoencoder and decision tree, then uses deep boosted regression trees to generate initial solution of UC-ED problem, and finally employs graph continuous cellular automaton (GCCA) to optimize the solution, achieving economic and secure power system dispatch. Compared with traditional algorithms, it achieves 1400

\times

harmonic mean speedup improvement, making it possible to solve large-scale problems; compared to the most up-to-date AI approaches, it can explicitly handle safety constraints. While achieving speed improvements, it reached economic optimality and, more importantly, achieved zero constraint violations. The experimental results on the IEEE 30-bus and IEEE 118-bus test systems demonstrate our achievements, indicating that ST-GCCA can find the optimal solution to the UC-ED problem.

机组承诺（UC）和经济调度（ED）是电力系统中的核心问题。UC-ED问题是一个大规模混合整数线性规划（MILP）问题。基于数学优化的算法不能解决大规模问题，基于启发式算法的模型容易陷入局部最优，基于深度学习的方法一般不能直接处理约束违规。为了解决UC-ED问题，提出了一个新的框架：基于空间树的图连续元胞自动机（ST-GCCA）。通过自编码器和决策树提取融合特征，然后利用深度增强回归树生成UC-ED问题的初始解，最后利用图连续元胞自动机（GCCA）对解进行优化，实现电力系统经济安全调度。与传统算法相比，实现了1400x的谐波平均加速提升，使解决大规模问题成为可能；与最新的人工智能方法相比，它可以显式地处理安全约束。在提高速度的同时，实现了经济最优，更重要的是，实现了零约束违规。在IEEE 30总线和IEEE 118总线测试系统上的实验结果验证了我们的成果，表明ST-GCCA可以找到UC-ED问题的最优解。

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引用次数: 0

Machine learning and deep learning techniques for detecting brown spot and narrow brown spot diseases in paddy (Oryza sativa): Algorithms, challenges, and future prospects 水稻褐斑病和窄褐斑病检测的机器学习和深度学习技术：算法、挑战和未来展望

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-02-02 DOI: 10.1016/j.ins.2026.123195

Fredy William Amon, Bhabesh Nath, Dhruba Kumar Bhattacharyya

The reviewed studies demonstrate that host plant resistance is a viable strategy for managing brown spot (BS) disease in paddy. However, challenges such as limited resistant varieties and environmental influences persist. Resistant genotypes like PARC-7 and IRRI-43 show promise, but breeding efforts must prioritise stability, yield, and genotype-environment interactions through multilocation testing. Accurate disease diagnosis, particularly distinguishing BS from narrow brown spot (NBS) based on lesion morphology, is critical for effective management. Meanwhile, AI-based disease monitoring presents opportunities but faces challenges in model selection, balancing accuracy with deployability. While advanced deep learning architectures show potential, issues such as lesion heterogeneity, data scarcity, and real-world variability hinder practical implementation. Future research must focus on robust data collection, improved image processing, lightweight AI models, and enhanced feature extraction to bridge the gap between controlled experiments and field applications. Addressing these challenges will be essential for developing reliable and scalable solutions to support sustainable rice production.

综上所述，寄主植物抗性是防治水稻褐斑病的一种可行策略。然而，诸如有限的抗性品种和环境影响等挑战仍然存在。抗性基因型如PARC-7和IRRI-43显示出希望，但育种工作必须通过多地点测试优先考虑稳定性、产量和基因型与环境的相互作用。准确的疾病诊断，特别是根据病变形态区分BS和窄棕色斑（NBS），对于有效的治疗至关重要。同时，基于人工智能的疾病监测提供了机遇，但在模型选择、平衡准确性和可部署性方面面临挑战。虽然先进的深度学习架构显示出潜力，但病变异质性、数据稀缺性和现实世界的可变性等问题阻碍了实际实施。未来的研究必须集中在稳健的数据收集、改进的图像处理、轻量级的人工智能模型和增强的特征提取上，以弥合控制实验和现场应用之间的差距。应对这些挑战对于制定可靠和可扩展的解决方案以支持可持续水稻生产至关重要。

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引用次数: 0

Campus anomaly detection systems from the perspective of unmanned aerial vehicles 基于无人机视角的校园异常检测系统

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-30 DOI: 10.1016/j.ins.2026.123166

Shujuan Feng , Jinming Wang , Yangkai Wu , Fei Liu , Ezzeddine Touti

Campus anomalies due to large crowds are monitored and thwarted using unmanned aerial vehicle (UAV) images/ videos. A pattern anomaly is detected by correlating the physical dimensions of the input image with the usual and unusual activities used for training. The proposed Boundary Position-induced Object Anomaly Detection (BPOAD) method uses deep ensemble learning with parallel, modular functions to identify unusual crowd behaviour patterns. The BPOAD method uniquely coordinates bagging and boosting processes within its method. The bagging component creates diverse training subsets to enhance boundary detection precision and orientation, while the boosting component adaptively weights misclassified instances to improve feature correlation across training sets. This dual approach allows the system to maintain high accuracy even after precision saturation, as the model can selectively apply either technique based on extracted features. This method establishes robust decision boundaries that maximise anomaly detection by correlating the physical dimensions of input images with normal and abnormal activity patterns. In real-world campus security applications, this significantly reduces false alarm rates and faster response times to potential threats. Experimental results demonstrate BPOAD’s effectiveness with 12.79% improved anomaly detection precision, 11.81% higher sensitivity, and 11.54% increased recall compared to existing methods. These improvements enable campus security personnel to more accurately identify and respond to unusual situations, ultimately enhancing overall campus safety management.

使用无人机（UAV）图像/视频监控和阻止大量人群导致的校园异常。通过将输入图像的物理尺寸与用于训练的通常和不寻常的活动相关联来检测模式异常。提出的边界位置诱导目标异常检测（BPOAD）方法使用深度集成学习与并行、模块化函数来识别异常的人群行为模式。BPOAD方法在其方法内唯一地协调装袋和提升过程。装袋组件创建不同的训练子集，以提高边界检测的精度和方向，而提升组件自适应加权错误分类的实例，以提高训练集之间的特征相关性。这种双重方法使系统即使在精度饱和后也能保持高精度，因为模型可以根据提取的特征选择性地应用任何一种技术。该方法通过将输入图像的物理尺寸与正常和异常活动模式相关联，建立了鲁棒的决策边界，最大限度地提高了异常检测。在现实世界的校园安全应用中，这大大降低了误报率，并加快了对潜在威胁的响应时间。实验结果表明，与现有方法相比，BPOAD的异常检测精度提高了12.79%，灵敏度提高了11.81%，召回率提高了11.54%。这些改进使校园保安人员能够更准确地识别和应对异常情况，最终提高校园整体安全管理水平。

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引用次数: 0

Generalized mining of mixed drove co-occurrence patterns 混合驱动共现模式的广义挖掘

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123148

Witold Andrzejewski, Pawel Boinski

In this paper, we investigate mining Mixed-Drove spatio-temporal Co-Occurrence Patterns (MDCOPs). MDCOPs represent sets of object types frequently located together for a given minimum fraction of time. Current solutions fail to address several important factors in practical applications. Specifically, state-of-the-art methods rely on a series of snapshots, i.e., discrete object positions recorded at predefined timestamps rather than their trajectories. However, spatio-temporal data gathering often depends on unsynchronized distributed sensors that independently register positions for each object.

To tackle this issue using traditional methods, one can interpolate object positions at snapshot timestamps. However, this raises another challenge: determining the optimal number of snapshots while balancing accuracy, processing time, and memory requirements. To overcome these limitations, we formulate a generalized MDCOP mining problem and introduce GMDCOP-Miner, an algorithm that employs a new, generalized time-prevalence measure. The proposed algorithm provides the most accurate results, equal to those obtained via state-of-the-art methods with the number of snapshots tending to infinity. Moreover, our experiments demonstrate that GMDCOP-Miner surpasses existing approaches in both processing time and memory efficiency.

本文研究了混合驱动时空共现模式（mdcop）的挖掘。mdcop表示在给定的最短时间内经常位于一起的对象类型集。目前的解决方案未能解决实际应用中的几个重要因素。具体来说，最先进的方法依赖于一系列快照，即在预定义的时间戳上记录的离散对象位置，而不是它们的轨迹。然而，时空数据的收集往往依赖于不同步的分布式传感器，这些传感器独立地记录每个物体的位置。要使用传统方法解决这个问题，可以在快照时间戳中插入对象位置。然而，这带来了另一个挑战：在平衡准确性、处理时间和内存需求的同时确定快照的最佳数量。为了克服这些限制，我们制定了一个广义MDCOP挖掘问题，并引入了GMDCOP-Miner，这是一种采用新的广义时间流行度量的算法。所提出的算法提供了最准确的结果，等于那些通过最先进的方法获得的快照数量趋于无穷大。此外，我们的实验表明，GMDCOP-Miner在处理时间和内存效率方面都优于现有的方法。

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引用次数: 0

Multi-objective two-archive evolutionary algorithm to optimize the discovery of gene networks involved in cancer survival 多目标双档案进化算法优化发现与癌症生存相关的基因网络

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-05-25 Epub Date: 2026-01-30 DOI: 10.1016/j.ins.2026.123182

Fernando M. Rodríguez-Bejarano , Sergio Santander-Jiménez , Miguel A. Vega-Rodríguez

Gene networks have gained considerable relevance in cancer research, enabling the representation of complex biological relationships that provide insights into the mechanisms driving tumor development and progression. The increasing availability of biological data facilitates the construction of clinically relevant gene networks by integrating multiple information sources. Specifically, we consider mutation data, patient survival data, and protein-protein interaction data to identify networks whose genes are recurrently mutated, significantly involved in patient survival, and functionally associated. To this end, we apply multi-objective optimization to simultaneously maximize survival impact, functional association, and mutation coverage. Herein, we introduce MOTEA-GENSU (Multi-Objective Two-archive Evolutionary Algorithm to discover GEne Networks involved in SUrvival), a novel method that employs two collaborative archives and intelligent evolutionary operators to guide the generation of high-quality gene networks. Evaluation across 27 real biological scenarios covering diverse cancer types shows that MOTEA-GENSU outperforms existing methods, achieving superior results in 92.6% of comparisons, with improvements of up to 315.8% over the best-performing competing approach, and consistently surpassing all state-of-the-art methods on average within each evaluated dataset. Biological analysis of the identified networks validates their functional coherence and significant impact on cancer patient survival, revealing clinically relevant networks composed of genes with demonstrated prognostic value.

基因网络已经在癌症研究中获得了相当大的相关性，使复杂的生物关系的表征能够深入了解驱动肿瘤发生和进展的机制。生物数据的可获得性不断提高，通过整合多种信息来源，促进了临床相关基因网络的构建。具体来说，我们考虑突变数据、患者生存数据和蛋白质-蛋白质相互作用数据，以确定基因反复突变、显著参与患者生存和功能相关的网络。为此，我们应用多目标优化来同时最大化生存影响、功能关联和突变覆盖。本文提出了MOTEA-GENSU （Multi-Objective two -archive Evolutionary Algorithm to discover involved in SUrvival GEne Networks）算法，该算法采用两个协同档案和智能进化算子来指导高质量基因网络的生成。对涵盖不同癌症类型的27种真实生物学情景的评估表明，MOTEA-GENSU优于现有方法，在92.6%的比较中取得了优异的结果，比表现最佳的竞争方法提高了315.8%，并且在每个评估数据集中平均持续超过所有最先进的方法。对已识别网络的生物学分析验证了它们的功能一致性和对癌症患者生存的重大影响，揭示了由具有预后价值的基因组成的临床相关网络。

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引用次数: 0