PMC:4331678 / 25514-26962
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"Comparing algorithms and evaluation metrics\nWe compared our proposed MNet with other related algorithms: ProMK [25], SW [16], OMG [27], LIG [28], and MSkNN [7]. MSkNN first trains a weighted majority vote [31] classifier (similar to a weighted kNN) on each individual network, and then integrates these classifiers for protein function prediction; it achieves competent performance on the first large-scale community based critical assessment of protein function annotation [2]. The details of the other comparing methods were introduced in the section of Related Work, and their parameter setting is discussed in the Additional File 1.\nThe quality of protein function prediction can be evaluated according to different criteria, and the choice of evaluation metrics differentially affects different prediction algorithms [2]. For a fair and comprehensive comparison, five evaluation metrics are used in this paper, namely MacroF1, MicroF1, Fmax, function-wise Area Under the Curve (fAUC ), and protein-wise AUC (pAUC ). These evaluation metrics are extensively applied to evaluate the performance of multilabel learning algorithms and protein function prediction [2,7,25,40]. More information about these evaluation metrics is provided in the Additional File 1. For an evaluation metric, since there are more than hundreds (or thousands) of labels for a dataset, a small performance difference between two comparing algorithms is also significant.","divisions":[{"label":"title","span":{"begin":0,"end":43}},{"label":"p","span":{"begin":44,"end":636}}],"tracks":[{"project":"2_test","denotations":[{"id":"25707434-20507895-14839086","span":{"begin":121,"end":123},"obj":"20507895"},{"id":"25707434-23514608-14839087","span":{"begin":157,"end":158},"obj":"23514608"},{"id":"25707434-11101803-14839088","span":{"begin":206,"end":208},"obj":"11101803"},{"id":"25707434-23353650-14839089","span":{"begin":475,"end":476},"obj":"23353650"},{"id":"25707434-23353650-14839090","span":{"begin":823,"end":824},"obj":"23353650"},{"id":"25707434-23353650-14839091","span":{"begin":1165,"end":1166},"obj":"23353650"},{"id":"25707434-23514608-14839092","span":{"begin":1167,"end":1168},"obj":"23514608"}],"attributes":[{"subj":"25707434-20507895-14839086","pred":"source","obj":"2_test"},{"subj":"25707434-23514608-14839087","pred":"source","obj":"2_test"},{"subj":"25707434-11101803-14839088","pred":"source","obj":"2_test"},{"subj":"25707434-23353650-14839089","pred":"source","obj":"2_test"},{"subj":"25707434-23353650-14839090","pred":"source","obj":"2_test"},{"subj":"25707434-23353650-14839091","pred":"source","obj":"2_test"},{"subj":"25707434-23514608-14839092","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#ecd193","default":true}]}]}}