# Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures
## 4 Results
### Quantitative Intrinsic Evaluation
#### 4.3.4 \(RQ_{co}\)  
Did Vectors of Domains with the Same GO Molecular Function Form Clusters in the \(V_{emb}\)?  
We parsed the GO annotation file of InterPro to extract first-level GO molecular function for domains for the four organisms. We followed the same methodology to examine the homogeneity of a \(V_{emb}\) with respect to GO molecular function annotations. For each \(V^{i}_{emb}\), we augmented each domain by its vector and its GO label, and we classified each domain using \(C^{d}_{nearest}\). As before, we used 5-fold stratified cross-validation for evaluation. In our experiments, we varied the number of neighbors \(k=\{2,5,20,40\}\) to test its influence on the change of performance.  
For space limitations, we summarized the performances showing only the best average accuracy over the number of neighbors. For ease of presentation, we omitted the result tables for the first three organisms and show only that for _Human_, but we discuss the results for all organisms. See Appendix D for full results.  
For _Malaria_, the best average accuracy was 76.86 (non-redundant, SKIP, \(w=5\), \(dim=100\), \(ep=40\)) and the minimum was 56.94 (non-overlapping, CBOW, \(w=5\), \(dim=100\), \(ep=10\)), presented in Table A4b,c respectively. We compared this moderate minimum accuracy to the maximum level of accuracy obtained by the randomized embedding space, which was 47.57 for \(dim=200\). Therefore, we concluded that _dom2 vec_ embeddings outperformed the random baseline by at least 10 percent.  
For _E. coli_, the best accuracy score was 81.72 (non-redundant, SKIP, \(w=5\), \(dim=50\), \(ep=5\)), and the minimum was 67.34 (non-overlapping, CBOW, \(w=2\), \(dim=200\), \(ep=5\)), shown in Table A5b,c respectively. Compared with the random baseline, achieving a best accuracy score of 64.46, we observed that, again, _dom2 vec_ was able to surpass the random baseline.  
For _Yeast_, the best accuracy score was 75.10 (non-redundant, SKIP, \(w=5\), \(dim=50\), \(ep=50\)), and the minimum accuracy value was 59.82 (non-overlapping, CBOW, \(w=5\), \(dim=50,ep=50\)), presented in Table A6b,c respectively. We contrasted this to the maximum accuracy level obtained in a random space, which was 53.73 (achieved for \(dim=100\)), to report that _dom2 vec_ vectors in \(V^{E,coli}_{emb}\) captured GO molecular function classes at a much higher degree than randomized vectors.  
For _Human_, the best average performance for non-redundant _InterPro annotations_ are shown in Table 1d. The best average accuracy level was 75.96, scored by 2-NN for \(V^{human}_{emb}\) (non-redundant, SKIP, \(w=5\), \(dim=50\), \(ep=40\)). The minimum accuracy value was 57.7, obtained by (non-overlapping, CBOW, \(w=2\), \(dim=50\), \(ep=10\)) shown in Table A7b. The best performance of a random space was 37.36 (Table A7b). We compared the minimum accuracy level of trained spaces with the best of the random spaces. We found that the minimum accuracy achieved in the _dom2 vec_ spaces was 20 percentage values higher than the best performance of the random space.  
For all four example organisms, we observed that the SKIP on non-redundant InterPro annotations produced \(V_{emb}\), in which \(C^{d}_{nearest}\) achieved the best average accuracy. For three out of the four organisms, the best performances were achieved for the lowest number of dimensions (\(dim=50\)). In all cases, we found that the worst-performing _dom2 vec_ embeddings outperformed the random baselines. By these findings, we affirmed that domain embeddings of the same GO molecular function class formed distinct clusters in the learned embedding space.