Abstract
The relationship between the genotype, defined as the set of genetic information encoded in the DNA, and the phenotype, defined as the macroscopic realization of that information, is still unclear. The emergence of a specific phenotype may be linked not only to gene expression, but also to environmental perturbations and experimental conditions. Moreover, even genetically identical cells in identical environments may display a variety of phenotypes. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic parameters or categories per specific genetic and/or environmental conditions as inputs. Furthermore, biological noise has been proven to play a crucial role in gene regulation mechanisms. The prediction of the average value of a given phenotype is not always sufficient to fully characterize a given biological system. In this study, we develop a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with a small number of observations per input condition. The key innovation of this study is that the deep neural network automatically generates the probability distributions based on only few (10 or less) noisy measurements for each input condition, with no prior knowledge or assumption of the probability distributions. This is extremely useful for exploring unknown biological systems with limited measurements for each input condition, which is linked not only to a better quantitative understanding of biological systems, but also to the design of new ones, as it is in the case of synthetic biology and cellular engineering.
Competing Interest Statement
The authors have declared no competing interest.
