Extracting Cellular Location of Human Proteins Using Deep Learning

TL;DR

This paper presents a deep learning model that automatically classifies the location of human proteins in cell images, achieving higher speed and accuracy than human experts, and enabling large-scale biomedical analysis.

Contribution

The authors developed a CNN-based classifier with Residue and Squeeze-Excitation layers that significantly improves protein localization accuracy and speed over previous methods.

Findings

Classifies 4,500 images per minute

Achieves 63.07% accuracy, surpassing human accuracy by 35%

Locates proteins in 28 subcellular structures across 27 cell types

Abstract

Understanding and extracting the patterns of microscopy images has been a major challenge in the biomedical field. Although trained scientists can locate the proteins of interest within a human cell, this procedure is not efficient and accurate enough to process a large amount of data and it often leads to bias. To resolve this problem, we attempted to create an automatic image classifier using Machine Learning to locate human proteins with higher speed and accuracy than human beings. We implemented a Convolution Neural Network with Residue and Squeeze-Excitation layers classifier to locate given proteins of any type in a subcellular structure. After training the model using a series of techniques, it can locate thousands of proteins in 27 different human cell types into 28 subcellular locations, way significant than historical approaches. The model can classify 4,500 images per minute with an accuracy of 63.07%, surpassing human performance in accuracy (by 35%) and speed. Because our system can be implemented on different cell types, it opens a new vision of understanding in the biomedical field. From the locational information of the human proteins, doctors can easily detect cell's abnormal behaviors including viral infection, pathogen invasion, and malignant tumor development. Given the amount of data generalized by experiments are greater than that human can analyze, the model cut down the human resources and time needed to analyze data. Moreover, this locational information can be used in different scenarios like subcellular engineering, medical care, and etiology inspection.