Fig. 3

The prediction approach mainly consists of two stages, feature selection with SHAP and hierarchical multi-label classification. Its inputs are a sub-hierarchy \(H'=(A',R')\), a subgraph of the GCN \(G'=(V',E',w)\), an annotation function \(\phi :V\rightarrow 2^{A'}\) that satisfy the sub-hierarchy \(H'\), the sub-matrices of \(J_G\) and \(J_F\) containing only the functions \(A'\) and genes \(V'\), and a constant value \(c\in [0,1]\) for feature selection. Its output is a function \(\psi : V' \times A'\rightarrow [0,1]\), which indicates for each gene \(v \in V'\), the probabilities \(\psi (v,a)\) of v being associated to function \(a\in A'\)