The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.

神经精神疾病的病因学涉及不同组学层面的复杂生物学过程，例如基因组学、转录组学、表观遗传学、蛋白质组学和代谢组学。高通量技术的出现以及大型开源数据集的可用性开创了系统生物学的新时代，需要整合各种类型的组学数据。生物机制的复杂性、整合策略的局限性以及多组学数据的异质性都对计算科学家提出了重大挑战。与早期和晚期集成相比，中间集成可以使用各种数据转换技术将每种数据类型转换为适当的中间表示，允许它捕获每个组学中包含的更多补充信息，并突出跨组学层的新交互。在这里，我们回顾了基于成分分析、矩阵分解、相似网络、多核学习、贝叶斯网络、人工神经网络和图变换的多模态中间集成技术，以及它们在神经精神病学领域的应用。我们描述了这些方法的进步，并比较了所检查的每种方法的优缺点。我们相信，我们的发现将有助于研究人员理解神经精神疾病中多组学数据的转换和整合。矩阵分解、相似网络、多核学习、贝叶斯网络、人工神经网络和图变换，以及它们在神经精神领域的应用。我们描述了这些方法的进步，并比较了所检查的每种方法的优缺点。我们相信，我们的发现将有助于研究人员理解神经精神疾病中多组学数据的转换和整合。矩阵分解、相似网络、多核学习、贝叶斯网络、人工神经网络和图变换，以及它们在神经精神领域的应用。我们描述了这些方法的进步，并比较了所检查的每种方法的优缺点。我们相信，我们的发现将有助于研究人员理解神经精神疾病中多组学数据的转换和整合。