Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.

Intestinal-type gastric cancer is preceded by premalignant lesions, including chronic atrophic gastritis and intestinal metaplasia. In this study, we constructed a single-cell atlas for 32,332 high-quality cells from gastric antral mucosa biopsies of patients spanning a cascade of gastric premalignant lesions and early gastric cancer (EGC) using single-cell RNA sequencing. We then constructed a single-cell network underlying cellular and molecular characteristics of gastric epithelial cells across different lesions. We found that gland mucous cells tended to acquire an intestinal-like stem cell phenotype during metaplasia, and we identified OR51E1 as a marker for unique endocrine cells in the early-malignant lesion. We also found that HES6 might mark the pre-goblet cell cluster, potentially aiding identification of metaplasia at the early stage. Finally, we identified a panel of EGC-specific signatures, with clinical implications for the precise diagnosis of EGC. Our study offers unparalleled insights into the human gastric cellulome in premalignant and early-malignant lesions.