Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel

Abstract

Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95–100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (PD-L1, CD274, PDCD1LG1, B7-H1) was validated in a series from the Tokai University Hospital by immunohistochemistry. In conclusion, artificial intelligence analysis of transcriptomic data of ulcerative colitis is a feasible analytical strategy.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

“…We recently modeled celiac disease and ulcerative colitis using AI [ 59 , 60 ]. In the case of ulcerative colitis, we analyzed a series of 43 cases, including 13 healthy controls, 8 inactive ulcerative colitis, 7 non-involved active ulcerative colitis, and 15 involved active ulcerative colitis.…”

Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA

“…GEO2R ran on R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, and limma 3.26.8. All the analyses were performed as previously described in our previous publications [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ]. The multilayer perceptron analysis is described in references [ 72 , 76 , 78 ].…”

“…Machine learning techniques are shown in references [ 75 , 79 , 80 ]. The method of analysis of this research is equivalent to the one recently published in ulcerative colitis [ 81 ].…”

Artificial intelligence and classification of mature lymphoid neoplasms

“…Therefore, weak AI successfully made lymphoma diagnosis without the use of histological images [ 15 ]. Of note, work on MCL was also carried out [ 32 ], and the same methodology was applied to non-tumour immunological conditions such as celiac disease and ulcerative colitis [ 28 , 29 ].…”

“…Additionally, the gene expression was used as a predictor of different lymphoma subtypes. Figure 2 shows the prediction of several mature B lymphoid neoplasms using an artificial neural network, the overall survival outcome (dead versus alive) using a Bayesian network, and the patients with DLBCL using transcriptomic data that highlighted ENO3 , MYC , and BCL2 genes [ 15 , 29 – 37 ]. AI has many applications, including the prediction of non-Hogkin lymphoma subtypes, and the prognosis of the patients.…”