  The high-content image-based assay is commonly leveraged for identifying the
phenotypic impact of genetic perturbations in biology field. However, a
persistent issue remains unsolved during experiments: the interferential
technical noise caused by systematic errors (e.g., temperature, reagent
concentration, and well location) is always mixed up with the real biological
signals, leading to misinterpretation of any conclusion drawn. Here, we show a
mean teacher based deep learning model (DeepNoise) that can disentangle
biological signals from the experimental noise. Specifically, we aim to
classify the phenotypic impact of 1,108 different genetic perturbations
screened from 125,510 fluorescent microscopy images, which are totally
unrecognizable by human eye. We validate our model by participating in the
Recursion Cellular Image Classification Challenge, and our proposed method
achieves an extremely high classification score (Acc: 99.596%), ranking the 2nd
place among 866 participating groups. This promising result indicates the
successful separation of biological and technical factors, which might help
decrease the cost of treatment development and expedite the drug discovery
process.
