Computational translation of genomic responses from experimental model systems to humans
Computational translation of genomic responses from experimental model systems to humans
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DOI：
10.1371/journal.pcbi.1006286
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发表时间：
2019-01-01
影响因子：
4.3
通讯作者：
Lauffenburger, Douglas A.
中科院分区：
文献类型：
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作者：
Brubaker, Douglas K.;Proctor, Elizabeth A.;Lauffenburger, Douglas A.
在小鼠模型中显示出转化为患者的前景的疗法的高失败率是生物医学科学中的一个紧迫挑战。虽然回溯性研究已经检验了小鼠模型对各自人类条件的保真度，但将小鼠模型的洞察力前瞻性地转化为患者的方法仍然相对未被探索。在这里，我们开发了一种半监督学习方法，用于从小鼠模型实验中推断与疾病相关的人类差异表达基因和途径。我们检查了36个转录学案例研究，其中可用于小鼠和人类炎症性疾病的表型可用，并评估了从小鼠数据集推断人类生物学的多种计算方法。我们发现，与直接解释老鼠实验相比，神经网络的半监督训练识别出更多真实的人类生物联系。对我们的模型最成功的小鼠实验的实验设计进行评估，揭示了实验设计的原则，可能会提高翻译性能。我们的研究表明，当在老鼠研究中前瞻性地评估生物相关性时，半监督学习方法结合老鼠和人类的数据进行生物推理，提供了对人类体内疾病过程的最准确的评估。最后，我们描述了四类由可用于分子洞察翻译的数据集的分辨率和覆盖率定义的模型系统到人类的“翻译问题”，并建议将从模型系统到人类疾病背景的洞察翻译任务可能通过具有翻译意识的实验设计和计算方法的组合来更好地完成。
The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.