检验免疫力计算模型--预测百日咳疫苗接种结果的特邀挑战赛

Pramod Shinde, Lisa Willemsen, Michael Anderson, Minori Aoki, Saonli Basu, Julie G Burel, Peng Cheng, Souradipto Ghosh Dastidar, Aidan Dunleavy, Tal Einav, Jamie Forschmiedt, Slim Fourati, Javier Garcia, William Gibson, Jason A Greenbaum, Leying Guan, Weikang Guan, Jeremy P Gygi, Brendan Ha, Joe Hou, Jason Hsiao, Yunda Huang, Rick Jansen, Bhargob Kakoty, Zhiyu Kang, James J Kobie, Mari Kojima, Anna Konstorum, Jiyeun Lee, Sloan A Lewis, Aixin Li, Eric F Lock, Jarjapu Mahita, Marcus Mendes, Hailong Meng, Aidan Neher, Somayeh Nili, Shelby Orfield, James Overton, Nidhi Pai, Cokie Parker, Brian Qian, Mikkel Rasmussen, Joaquin Reyna, Eve Richardson, Sandra Safo, Josey Sorenson, Aparna Srinivasan, Nicky Thrupp, Rashmi Tippalagama, Raphael Trevizani, Steffen Ventz, Jiuzhou Wang, Cheng-Chang Wu, Ferhat Ay, Barry Grant, Steven H Kleinstein, Bjoern Peters
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引用次数: 0

摘要

系统疫苗学研究已被用于建立预测个体疫苗反应的计算模型,并确定导致结果差异的因素。由于研究设计的差异性,比较此类模型具有挑战性。为了解决这个问题,我们建立了一个社区资源,用于比较预测百日咳强化免疫反应的模型,并生成实验数据,以明确模型评估的目的。我们在此介绍利用该资源进行的第二次计算预测挑战,我们对来自 53 位科学家的 49 种算法进行了基准测试。我们发现,最成功的模型在处理非线性、将大型特征集缩减为代表性子集以及高级数据预处理方面表现突出。与此相反,我们发现从文献中提取的用于预测其他情况下疫苗抗体反应的模型表现不佳,这更加说明了建立专门模型的必要性。总之,这证明了专门生成的数据集对严格和公开的模型评估的价值,以确定可提高疫苗反应预测中计算模型的可靠性和适用性的特征。
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Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes
Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.
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