Heterogeneous　data　sources　and　multi-label　are　two　important　characteristics　of　protein　function　prediction.　They　describe　protein　data　from　two　different　aspects.　However,　it　is　of　considerable　challenge　to　integrate　multiple　data　sources　and　multi-label　simultaneously　for　predicting　protein　functions,　especially　when　there　are　only　a　limited　number　of　labeled　proteins.　In　this　paper,　we　propose　a　generative　model　with　hypergraph　regularizers　algorithm,　called　GMHR,　for　predicting　proteins　with　multiple　functions.　The　GMHR　algorithm　integrates　all　data　sources　that　are　available,　including　protein　attribute　features,　interaction　networks,　label　correlations,　and　unlabeled　data.　Experimental　results　on　the　real-world　datasets　predicting　the　functions　of　proteins　demonstrate　the　superiority　of　our　proposed　method　compared　with　the　state-of-the-art　baselines.