Generative-discriminative hybrids for disease prediction and cell communication modelling

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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.