Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE, 41296, Sweden.
NPJ Syst Biol Appl. 2024 Jan 29;10(1):13. doi: 10.1038/s41540-024-00341-9.
The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate human biology as evidenced by the predominant likelihood of clinical trial failure. To address this problem, we developed AutoTransOP, a neural network autoencoder framework, to map omics profiles from designated species or cellular contexts into a global latent space, from which germane information for different contexts can be identified without the typically imposed requirement of matched orthologues. This approach was found in general to perform at least as well as current alternative methods in identifying animal/culture-specific molecular features predictive of other contexts-most importantly without requiring homology matching. For an especially challenging test case, we successfully applied our framework to a set of inter-species vaccine serology studies, where 1-to-1 mapping between human and non-human primate features does not exist.
治疗学和疫苗的开发需要对人类生物学有系统的了解。尽管动物和体外培养模型可以阐明一些疾病机制，但它们通常不能充分重现人类生物学，这表现在临床试验失败的主要可能性上。为了解决这个问题，我们开发了 AutoTransOP，这是一个神经网络自动编码器框架，将来自指定物种或细胞背景的组学图谱映射到一个全局潜在空间中，从中可以识别出不同背景下的相关信息，而无需通常强加的同源物匹配要求。一般来说，这种方法在识别动物/培养物特有的分子特征方面至少与当前的替代方法表现一样好，这些特征可预测其他背景——最重要的是，无需同源匹配。对于一个特别具有挑战性的测试案例，我们成功地将我们的框架应用于一组物种间疫苗血清学研究，在这些研究中，人类和非人类灵长类动物特征之间不存在一对一映射。
