MMAPLE dataset v1.0

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We developed a semi-supervised meta learning framework - Meta Model Agnostic Pseudo Label Learning (MMAPLE) - to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when transfer learning fails. The power of MMAPLE is demonstrated in multiple applications: predicting OOD drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions, where chemicals or proteins in unseen data are dramatically different from those in training data. MMAPLE achieves 11\% to 242\% improvement in the prediction-recall on multiple OOD benchmarks over baseline models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by bioactivity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

受实验技术局限与人类认知偏差影响，诸多生物学问题尚未得到充分研究。尽管深度学习在加速科学发现领域颇具潜力，但当应用于标注数据稀缺且存在数据分布偏移的任务时，其性能会受到显著削弱。为此，我们研发了一款半监督元学习框架——模型不可知伪标签学习（Meta Model Agnostic Pseudo Label Learning, MMAPLE），可在迁移学习失效的场景下，通过有效挖掘分布外（out-of-distribution, OOD）未标注数据来应对上述挑战。MMAPLE的优异性能已在多项应用中得到验证：包括预测分布外药物-靶点相互作用、隐藏的人类代谢物-酶相互作用，以及尚未得到充分研究的跨物种微生物组代谢物-人类受体相互作用。此类任务的待预测数据中，化学物质或蛋白质与训练数据内的同类物质存在显著差异。相较于基线模型，MMAPLE在多项分布外基准测试集上的预测召回率提升了11%至242%。借助MMAPLE，我们发现了全新的跨物种代谢物-蛋白质相互作用，这些相互作用已通过生物活性实验得到验证，同时填补了微生物组-人类相互作用网络中的缺失关联。MMAPLE作为一款通用框架，可用于探索当前实验与计算技术尚未触及的、此前未被认知的生物学领域。