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NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information
[article]
2018
bioRxiv
pre-print
AbstractAutomated function prediction (AFP) of proteins is of great significance in biology. In essence, AFP is a large-scale multi-label classification over pairs of proteins and GO terms. Existing AFP approaches, however, have their limitations on both sides of proteins and GO terms. Using various sequence information and the robust learning to rank (LTR) framework, we have developed GOLabeler, a state-of-the-art approach of CAFA3, which overcomes the limitation of the GO term side, such as
doi:10.1101/439554
fatcat:4g7hgdcbsbfvzhzwdavyfoq54q