Deep Learning for Parkinson’s Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein–Peptide Datasets

. 2024 Sep 29;14(19):2181. doi: 10.3390/diagnostics14192181

Algorithm 2: GNN for Data Prediction

1 : P r o c e d u r e f o r G N N P r e d i c t i o n

2 : L \leftarrow N u m b e r o f l A y e r s i n G N N

3 : V \leftarrow S t a t i c N o d e i n g r a p h G

4 : E \leftarrow S t a t i c E d g e s i n g r a p h G

I n i t i a l i z a t i o n o f a l l p a r a m a t e r s

5 : f o r v \in V d o

6 : h_{v}^{0} \leftarrow [x_{v}, 0, \dots, 0]

7 : f o r e \in E d o

8 : g_{e}^{0} \leftarrow [z_{v}, 0, \dots, 0]

L a y e r w i s e G N N P r o c e s s

9 : f o r l = 1 t o L d o

E d g e P r o c e s s i n g : I n t e r c h a n g i n g t h e n o d e a n d e d g e o r d e r

10 : f o r e \in E d o

11 : b_{e}^{l} = ρ_{E}^{l} (\{g_{e}^{l - 1}, h_{v}^{l - 1} : u \in N (e)\})

12 : g_{e}^{l} = \emptyset_{E}^{l} (\{b_{e}^{l}\})

N o d e P r o c e s s i n g : I n t e r c h a n g i n g t h e n o d e a n d e d g e o r d e r

13 : f o r v \in V d o

14 : a_{v}^{l} = ρ_{V}^{l} (\{h_{v}^{l - 1}, h_{v}^{l - 1} : u \in N (v)\})

15 : h_{v}^{l} = \emptyset_{V}^{l} (\{a_{v}^{l}\})

R e a d o u t :

16 : \hat{y} = \emptyset_{G} (ρ_{G} (\{h_{v}^{l}, g_{e}^{l} : v, e \in G\}))