DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.

ABSTRACT: Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper-parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data wo

SUBMITTER: Sureyya Rifaioglu A 

PROVIDER: S-EPMC6517386 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature