The results of DCN-based aggregated neighbor-counting method on 66 randomly selected proteins.

The ways of calculating precision and recall can be found at the captions of Table 4 and Table 3, respectively. Explanations of the best semantic similarity score can be found at the caption of Table 2. From the 100 proteins randomly selected from GO database, 66 proteins have predictions available by DCN-based aggregated neighbor-counting method.精确率（precision）与召回率（recall）的计算方式分别详见表4与表3的图例说明。最佳语义相似度得分（semantic similarity score）的相关解释详见表2的图例说明。从基因本体（Gene Ontology, GO）数据库中随机选取的100个蛋白质中，有66个可通过基于DCN的聚合邻域计数方法获得预测结果。