Decoding multilevel relationships with the human tissue-cell-molecule network

Abstract

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

“…"Multi-level modular relationship" law could be thus uncovered and summarized among biological elements at various hierarchical levels. Predictive algorithms have been established for disease-causing genes and drug targets with higher accuracy than popular methods, and used to systematically analyze disease network regulation mechanisms under specific tissue or cell conditions, thus achieving systematic integration of multi-level information such as phenotype-cell-molecule modules 60,61 . GC malignant progression involves intricate multi-level information that corresponds to dynamic evolution at various pathological stages, such as CAG, IM, and LGD.…”

“…Machine learning approaches are used to satisfy the need to appropriately incorporate biological knowledge hidden at different levels, such as gene regulation mechanisms, into models; in contrast, statistical methods tend to ignore the details of biological relationships in attempts to explain most variations by using only a few surrogate parameters. By incorporating AI methods, network analysis of gastric inflammation-induced tumorigenesis can enable the elucidation of intricate relationships among various factors at multiple levels, including genes, cells, pathways, and phenotypes [59][60][61][120][121][122] . In summary, integrating information across levels and developing more sophisticated models will be key to advancing understanding of the complex processes underlying malignant progression.…”

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

“…Furthermore, Hou et al. [ 65 ] integrated diseases/syndromes phenotypes, tissues, cell types and molecular interaction data and established a human multilevel heterogeneous biological network based on a graph embedding algorithm, achieving high performance in multiple tasks.…”

“…[ 60 ] established a CPN, providing a basis for personalized diagnosis and treatment of TCM. From a micro perspective, we can achieve the integration of disease diagnosis and syndrome differentiation by performing relevant gene prediction or omics data analysis based on the disease phenotypes and syndrome characteristics, followed by the construction of similarity metrics and correlation analysis based on biological molecular networks [ 40 , 42 , 65 , 91–93 , 94 ]. For example, mining the relationships between Cold/Hot syndromes and neuro-endocrine-immune (NEI) biological networks [ 91 ], analyzing the biological basis of spleen deficiency syndrome and its relationships with digestive diseases [ 92 ], analyzing the biological basis of PBS syndrome in coronary heart disease (CHD) and establishing diagnostic markers to achieve ‘same disease with different treatments’ [ 42 ].…”

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

“…In recent years, advances in computational methods for networks and graphs have made it possible to extract interconnected features of nodes from complex biological networks [11] . The random walk-based graph embedding algorithm implements a mapping from a high-dimensional network to a low-dimensional vector [12] .…”