A deep neural network approach for the prediction of protein subcellular localization

Abstract

The subcellular localization of proteins is an essential characteristic of human cells, which plays a vital part in understanding distinct functions and cells' biological processes. The abnormal protein subcellular localization affects protein functionality and may cause many human diseases ranging from metabolic disorders to cancer. Therefore, the prediction of subcellular locations of the proteins is an important task. Artificial neural network has become a popular research topic in machine learning that can achieve remarkable results in learning highlevel latent traits. This paper proposes a deep neural network (DNN) model to predict the human protein subcellular locations. The DNN automatically learns high-level representations of abstract features and proteins by examining nonlinear relationships between different subcellular locations. The experimental results have shown that the proposed method gave better results compared with the classical machine learning techniques such as support vector machine and random forest. This model also outperformed the similar model, which uses stacked auto-encoder (SAE) with a softmax classifier.

Motivation, Definition, Application and the Future of Edge Artificial Intelligence

“…As we explain in this section, Edge AI need not to be confined to operating the models of AI just on the edge devices or servers. According to a dozen recent research, running Deep Neural Network (DNN) models [3] with edge-cloud synergy decreases end-to-end delay and consumption of energy in contrast to the operating them on a local level. There are various purposes as to why we have a belief on a collaborative hierarchy being integrated into the establishing of cutting-edge information gathering technology systems.…”