School of Computer and Control Engineering, Yantai University, Yantai 264005, China.
Sensors (Basel). 2024 May 21;24(11):3289. doi: 10.3390/s24113289.
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.
生物传感器在检测癌症信号方面发挥着至关重要的作用，通过协调一系列复杂的生物和物理转换过程来实现这一目标。在各种癌症中，乳腺癌因其遗传基础而引人注目，这种遗传基础引发了不受控制的细胞增殖，主要影响女性，并导致高死亡率。生物传感器在预测生存时间方面的应用对于制定最佳治疗策略至关重要。然而，传统的生物传感器在学习任务的特征预处理方面采用传统的机器学习方法会遇到挑战。尽管深度学习技术具有自动提取有用特征的潜力，但它们往往难以有效地利用特征和实例之间的复杂关系。为了解决这一挑战，我们的研究提出了一种新颖的智能生物传感器架构，该架构集成了一种多视图多向图学习（MVMWGL）方法，用于预测乳腺癌的生存时间。这种创新方法能够吸收来自基因相互作用和生物传感器相似性的见解。通过利用真实世界的数据，我们进行了全面的评估，实验结果明确表明 MVMWGL 方法优于现有方法。
