A hierarchical multi-label classification method based on neural networks for gene function prediction

Figures & data

Figure 1. A fraction of the GO taxonomy. Reprinted from [24].

Figure 2. Architecture of each MLP.

Table 1. The characteristics of the experimental data sets.

Data set	Attributes	Training instances	Testing instances	Number of GO terms
D1 Cellcycle	77	2290	1202	168
D2 eisen	79	2252	1182	125
D3 derisi	63	1537	817	165
D4 gasch1	173	2294	1205	168
D5 gasch2	52	1301	1212	168
D6 church	27	2289	1202	168
D7 spo	80	2354	1183	165
D8 seq	478	2321	1225	168

Figure 3. Results of hF₁ for eight data sets: macro-averaged version (a) and micro-averaged version (b).

Feng S, Fu P, Zheng WB. A hierarchical multi-label classification algorithm for gene function prediction. Algorithms. 2017;10(4):138. DOI:10.3390/a10040138

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