AI Network Validations

biological validations of individual ai networks to verify the tissue/disease relevance of the ai networks, go term enrichment analysis and hub gene detection and evaluation were performed the networks analyzed were; the gastrointestinal – ai ulcerative colitis (uc) network, the breast – ai breast cancer/tissue network, and the blood – ai general network first, we used the louvain algorithm (resolution = 1 0, minimum module size = 10 genes) to detect important modules in the network top hub genes of each module were detected, ranked by their degree of centrality go term enrichment analysis of the genes within each module was performed using enrichr (1), and the results were evaluated for disease and/or tissue relevant biology results gastrointestinal – ai ulcerative colitis network in the ai uc network, eight modules were identified pathway enrichment results of some of these modules showed key pathogenic functions, such as; ubiquitin dependent protein catabolic process, immune system, and neutrophil degranulation (table 1 and 2) this indicates that vital uc disease biology can be captured based on the ai networks table 1 ai uc network, module 1, top 10 significantly enriched reactome terms term adjusted p value immune system (r hsa 168256) 7 87e 64 innate immune system (r hsa 168249) 4 52e 46 neutrophil degranulation (r hsa 6798695) 8 49e 42 metabolism of proteins (r hsa 392499) 4 06e 40 vesicle mediated transport (r hsa 5653656) 7 73e 37 membrane trafficking (r hsa 199991) 7 09e 36 metabolism (r hsa 1430728) 6 67e 34 table 2 ai uc network, module 1, top 10 significantly enriched go biological process (bp) terms term adjusted p value ubiquitin dependent protein catabolic process (go 0006511) 1 02e 22 intracellular protein transport (go 0006886) 1 22e 20 protein transport (go 0015031) 1 67e 17 golgi vesicle transport (go 0048193) 1 44e 16 proteasome mediated ubiquitin dependent protein catabolic process (go 0043161) 2 15e 16 proteasomal protein catabolic process (go 0010498) 4 48e 16 negative regulation of programmed cell death (go 0043069) 6 53e 16 oxidative phosphorylation (go 0006119) 9 21e 15 breast – ai breast cancer/tissue network in the ai breast cancer network, nine modules were identified pathway analysis of the largest of these modules showed functions such as cilium assembly and cilium organization (table 3) these functions, which are frequently inhibited or lost in breast cancer, can contribute to cancer progression by promoting cell proliferation and metastasis another module showed important immune /cancer related functions, such as inflammatory response, cellular response to cytokine stimulus, and antigen receptor mediated signaling pathway (table 4) these pathways are highly relevant to the tumor immune microenvironment and suggest this module may be involved in immune cell infiltration and inflammatory signaling in breast cancer furthermore, one module showed enrichment of the cell cycle pathway, which is a hallmark of cancer one of the hub genes within this module, bub1, is also a well known driver gene for breast cancer these results indicate important cancer relevant processes being captured by the ai breast cancer network table 3 ai breast cancer network, module 1, top 10 significant enrichment go biological process (bp) terms term adjusted p value cilium assembly (go 0060271) 3 16e 10 cilium organization (go 0044782) 3 16e 10 trna modification (go 0006400) 3 16e 10 plasma membrane bounded cell projection assembly (go 0120031) 9 37e 06 organelle assembly (go 0070925) 2 26e 05 trna processing (go 0008033) 0 00319 table 4 ai breast cancer network, module 4, top 10 significant enrichment go biological process (bp) terms term adjusted p value antigen receptor mediated signaling pathway (go 0050851) 5 05e 23 inflammatory response (go 0006954) 7 89e 22 cellular response to cytokine stimulus (go 0071345) 1 34e 21 extracellular matrix organization (go 0030198) 2 41e 19 positive regulation of cytokine production (go 0001819) 9 69e 17 blood – ai general network in the ai blood network, 10 modules were identified this network includes a diverse collection of blood diseases including myeloid leukemia, pneumonia, and covid 19 thus, in the enrichment results, we observed functions involved in regulating viral replication and immune responses, such as mrna splicing, via spliceosome, innate immune system and neutrophil degranulation (table 5 and 6) one module additionally showed important hub genes, melk, bub1, chek1 these are critical cell cycle checkpoints, frequently dysregulated in myeloid leukemia and other blood cancers, where they contribute to uncontrolled cell proliferation and genomic instability table 5 ai blood network, module 0, top 10 significant enrichment go biological process (bp) terms term adjusted p value gene expression (go 0010467) 3 50e 41 ubiquitin dependent protein catabolic process (go 0006511) 3 56e 39 cytoplasmic translation (go 0002181) 3 19e 33 proteasome mediated ubiquitin dependent protein catabolic process (go 0043161) 4 79e 33 positive regulation of dna templated transcription (go 0045893) 4 41e 31 mrna processing (go 0006397) 1 60e 29 mrna splicing, via spliceosome (go 0000398) 1 52e 28 proteasomal protein catabolic process (go 0010498) 4 13e 28 table 6 ai blood network, module 0, top 10 significant enrichment reactome terms term adjusted p value immune system (r hsa 168256) 7 80e 132 innate immune system (r hsa 168249) 1 30e 102 neutrophil degranulation (r hsa 6798695) 8 15e 92 cellular responses to stress (r hsa 2262752) 9 20e 88 infectious disease (r hsa 5663205) 1 55e 87 metabolism of rna (r hsa 8953854) 4 27e 86 cellular responses to stimuli (r hsa 8953897) 2 61e 85 disease (r hsa 1643685) 6 69e 67 cytokine signaling in immune system (r hsa 1280215) 2 30e 59 processing of capped intron containing pre mrna (r hsa 72203) 1 45e 54