Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction
Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction
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DOI：
10.1093/bib/bbad364
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发表时间：
2023-09-22
影响因子：
9.5
通讯作者：
Guo，Wei-Feng
中科院分区：
文献类型：
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作者：
Liang，Jing;Li，Zong-Wei;Guo，Wei-Feng
考虑到癌症是由个体患者的几个必需基因的计算引起的，研究人员开始关注通过个性化边缘网络分析来识别个性化边缘网络生物标志物（PEBs）。然而，在多目的早期疾病预测（MPEDP）中，当存在多模态生物标志物时，现有的方法大多忽略了PEBs的优化。为了解决这一问题，本研究提出了一种新的模型（MMPDENB-RBM），该模型将个性化动态边缘网络生物标志物（PDENB）理论、多模态优化策略和潜在空间搜索方案相结合，用于识别不同PDENB模块配置的生物标志物（即有效识别多模态PDENB）。通过对The cancer Genome Atlas数据库中三个最大的癌症组学数据集（乳腺浸润性癌、肺鳞状细胞癌和肺腺癌）的应用表明，与其他先进方法相比，MMPDENB-RBM模型可以更有效地预测癌症的临界状态。与现有方法相比，该模型具有更好的收敛性、多样性和多峰性，并具有有效的优化能力。特别是，鉴定的多模态pdenb同时富含不同的功能生物标志物，如组织特异性合成致死边缘生物标志物，包括癌症驱动基因和疾病标记基因。重要的是，作为我们的目标，这些多模态生物标志物可以在药物靶点筛选，生存风险评估和新的生物医学视野中发挥不同的生物学和生物医学意义，作为个性化早期疾病预测的预期多用途。综上所述，本研究提供了pdenb的多模态特性，特别是具有更多生物学意义的治疗性生物标志物，可以帮助个体癌症患者的MPEDP。
Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.