对胃癌的前癌、早期和晚期状态有详细了解，有助于开发更好的风险模型，以及拦截癌变进程的医学治疗。我们建立了一个胃癌布尔蕴含网络，并应用机器学习算法开发预测已知前癌状态（如萎缩性胃炎、肠化生和低到高级肠癌（L/HGIN）以及胃癌）的模型。我们的方法利用了不对称的布尔蕴含关系的存在，这些关系可能在几乎所有胃癌数据集中都是不变的。不变的不对称布尔蕴含关系可以解读生物数据的基本时间序列。在这种方法的推动下，我们开发了基于健康粘膜与GC之间连续的模型。我们的模型对于区分健康和GC样本的公开模型表现更加优异。虽然没有在肠化生和L/HGIN的数据集上进行训练，但该模型可以通过肠上皮细胞萎缩→异型增生→新生物的级联反应来识别GC进展的风险。该模型可以对所有公开的小鼠模型进行排序，以评估它们在模拟人类GC启动和进展时基因表达模式上的表现。布尔蕴含网络使研究人员能够确定GC启动期间迄今为止未定义的连续状态。开发出的模型现在可以作为理性设计拦截GC进展的候选治疗靶点的起点。 ©2023年作者 。

Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.© 2023. The Author(s).