Generative-discriminative hybrids for disease prediction and cell communication modelling

我们的目标是研究机器学习的新进展和专业知识，以提高从基因组和蛋白质组数据预测疾病的可靠性，并能够回答有关疾病发展的细胞间通讯机制的新生物学问题。统计机器学习方法已经被证明在原则上对这些目标有很大的希望。然而，高通量技术导致越来越高维的数据，而样本的数量仍然有限。这些极端条件的影响在很大程度上被现有技术所忽视。此外，目前现有技术无法解决的新生物学问题正在被提出。机器学习的最新成果使解决这些问题成为可能。特别是，混合生成和判别模型可以混合两者的好处，并可以减少所需的样本量。距离函数和数据模型的明智选择可以减轻维度灾难。调整以前开发的社会网络推理的某些技术可能提供所需的建模能力，推断细胞间的通信mechanism.By探索这些技术的潜力，我们希望为生命科学家创造新的和改进的计算方法铺平道路。

We aim to investigate new advances and expertise in machine learning to improve the reliability of disease prediction from genomic and proteomic data, and to enable answering novel biological questions regarding cell-cell communication mechanisms that underlie the development of disease.Statistical machine learning methods have already been shown to hold a lot of promise towards these goals in principle. However, high-throughput technologies result in increasingly high dimensional data, while the number of samples remains limited. The implications of these extreme conditions are largely overlooked by the existing state of the art. In addition, new biological questions are being asked that currently existing techniques are unable to tackle.Recent results in machine learning make it possible to address these issues. In particular, hybridizing generative and discriminative models may blend the benefits of both and may reduce the required sample size. An informed choice of distance functions and data models may mitigate the curse of dimensionality. Adapting certain techniques previously developed for social network inference may provide the required modelling power for inferring cell-cell communication mechanisms.By exploring the potential of these techniques, we hope to pave the way towards creating novel and improved computational methods for life scientists.