AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

ABSTRACT: It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed me

SUBMITTER: Usman M 

PROVIDER: S-EPMC8928959 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature