Publication Type : Journal Article
Publisher : Czech Technical University in Prague - Central Library
Source : Neural Network World
Url : https://doi.org/10.14311/nnw.2021.31.002
Campus : Amaravati
School : School of Computing
Year : 2021
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 high- level 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.
Cite this Research Publication : Anosh Babu P. Samson, Sekhara Rao Annavarapu Chandra, Mani Manikant, A deep neural network approach for the prediction of protein subcellular localization, Neural Network World, Czech Technical University in Prague - Central Library, 2021, https://doi.org/10.14311/nnw.2021.31.002