GODoc: high-throughput protein function prediction using novel k-nearest-neighbor and voting algorithms.

ABSTRACT:

Background

Biological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments. Gene Ontology (GO) is a framework for unifying the representation of protein function in a hierarchical tree composed of GO terms.

Results

We propose GODoc, a general protein GO prediction framework based on sequence information which combines feature engineering, feature reduction, and a novel ​k​-nearest-neighbor algorithm to resolve the multiple GO prediction problem. Comprehensive evaluation on CAFA2 shows that GODoc performs better than two baseline models. In the CAFA3 co

SUBMITTER: Liu YW 

PROVIDER: S-EPMC7672824 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature