跨生物推断的不变风险最小化：在人类风险因素发现中用小鼠数据代替人类数据
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
摘要：
由于数据隐私问题，难以进行某些类型的实验或高昂的相关成本，获得人类医疗数据可能具有挑战性。在许多情况下，来自动物模型或体外细胞系的数据可以帮助我们增强对人类数据的理解。然而，与人类数据相比，该数据已知具有较低的病因学有效性。在这项工作中，我们用体外数据和动物模型来增强小型人类医学数据集。我们使用不变风险最小化（Invariant Risk Minimisation，简称RST），通过考虑跨生物体数据属于不同的数据生成环境来阐明不变特征。我们的模型确定了与人类癌症发展相关的基因。我们观察到不同数量的人类和小鼠数据之间的一致性程度，但是，需要进一步的工作来获得结论性的见解。作为次要贡献，我们增强了现有的开源数据集，并向社区提供了两个统一处理的、跨生物体的、同源基因匹配的数据集。
英文摘要：
Human medical data can be challenging to obtain due to data privacy concerns, difficulties conducting certain types of experiments, or prohibitive associated costs. In many settings, data from animal models or in-vitro cell lines are available to help augment our understanding of human data. However, this data is known for having low etiological validity in comparison to human data. In this work, we augment small human medical datasets with in-vitro data and animal models. We use Invariant Risk Minimisation (IRM) to elucidate invariant features by considering cross-organism data as belonging to different data-generating environments. Our models identify genes of relevance to human cancer development. We observe a degree of consistency between varying the amounts of human and mouse data used, however, further work is required to obtain conclusive insights. As a secondary contribution, we enhance existing open source datasets and provide two uniformly processed, cross-organism, homologue gene-matched datasets to the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
