Hierarchical deep learning for predicting GO annotations by integrating protein knowledge.

ABSTRACT:

Motivation

Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming, and cannot cope with the exponential growth of data generated by high throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional pred

SUBMITTER: Merino GA 

PROVIDER: S-EPMC9524999 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature