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

Carreras, Joaquim

doi:10.3390/healthcare10081550

Cited by 11 publications

(16 citation statements)

References 94 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Carreras

Roncador

Hamoudi

2022

Cancers

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Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

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Section: Discussionmentioning

confidence: 99%

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

Carreras

Roncador

Hamoudi

2022

Cancers

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“…The description of the different techniques is present in the original publications [ 28 – 37 ]. The C5 decision tree predicts only categorical variables; it is characterized as being robust when there is missing data or the model includes a large number of predictors, the training time is relatively short, and has a simple interpretation.…”

Section: Applications Of Ai In the Classification And Prognosis Of B ...mentioning

confidence: 99%

“…For instance, a Bayesian network can be constructed to predict a specific disease based on the presence of different symptoms or data. If information is unavailable, the Bayesian networks are incredibly resilient and produce the best feasible forecast using whatever information is available [ 28 – 37 ].…”

Section: Applications Of Ai In the Classification And Prognosis Of B ...mentioning

confidence: 99%

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Artificial intelligence and classification of mature lymphoid neoplasms

Carreras,

Hamoudi,

Nakamura

2024

Exploration of Targeted Anti-Tumor Therapy

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Hematologists, geneticists, and clinicians came to a multidisciplinary agreement on the classification of lymphoid neoplasms that combines clinical features, histological characteristics, immunophenotype, and molecular pathology analyses. The current classification includes the World Health Organization (WHO) Classification of tumours of haematopoietic and lymphoid tissues revised 4th edition, the International Consensus Classification (ICC) of mature lymphoid neoplasms (report from the Clinical Advisory Committee 2022), and the 5th edition of the proposed WHO Classification of haematolymphoid tumours (lymphoid neoplasms, WHO-HAEM5). This article revises the recent advances in the classification of mature lymphoid neoplasms. Artificial intelligence (AI) has advanced rapidly recently, and its role in medicine is becoming more important as AI integrates computer science and datasets to make predictions or classifications based on complex input data. Summarizing previous research, it is described how several machine learning and neural networks can predict the prognosis of the patients, and classified mature B-cell neoplasms. In addition, new analysis predicted lymphoma subtypes using cell-of-origin markers that hematopathologists use in the clinical routine, including CD3, CD5, CD19, CD79A, MS4A1 (CD20), MME (CD10), BCL6, IRF4 (MUM-1), BCL2, SOX11, MNDA, and FCRL4 (IRTA1). In conclusion, although most categories are similar in both classifications, there are also conceptual differences and differences in the diagnostic criteria for some diseases. It is expected that AI will be incorporated into the lymphoma classification as another bioinformatics tool.

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“…Celiac disease (CD) is an autoimmune gluten-sensitive enteropathy, which results in small intestinal lesions and malabsorption in affected cases [9]. The pathogenesis of CD is based on genetic factors and mucosal immune response [10]. Almost all affected patients with CD have HLA DR3-DQ2 and/or the DR4-DQ8 [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Is Celiac Disease (CD) Prevalent in Patients with Multiple Sclerosis (MS): A Systematic Review and Meta-Analysis

Olfati

Ghoshouni

Ebrahimi

et al. 2022

Multiple Sclerosis International

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Background. Celiac disease (CD) is an autoimmune disease, and its prevalence reported variously in different studies. The goal of this study is to evaluate the pooled prevalence of CD in subjects with MS. Methods. PubMed, Scopus, EMBASE, Web of Science, and Google Scholar along with gray literature were systematically searched. The search included all relevant studies which were published up to October 2022. Two researchers independently searched all databases and also references of included studies. Results. We found 8211 articles by literature search, and after deleting duplicates, 5594 remained. Fifteen articles remained for meta-analysis. Totally, 31418 patients were evaluated, and the total number of possible/confirmed cases was 124. Studies were published between 2004 and 2020, and the most published studies were from Italy. Five studies provided information regarding controls. The total number of controls was 22394, of whom 22 had CD. Mean age ranged from 35 to 55 years. The pooled prevalence of CD in MS patients was 0 ( I 2 = 88.2 %, p < 0.001 ). The pooled odds of CD in subjects with MS are 0.46 (95% CI: 0.19-1.1) ( I 2 = 0 , p = 0.9 ). Conclusion. The pooled prevalence of this systematic review showed that CD is not prevalent in MS cases.

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

Cited by 11 publications

References 94 publications

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

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

Artificial intelligence and classification of mature lymphoid neoplasms

Is Celiac Disease (CD) Prevalent in Patients with Multiple Sclerosis (MS): A Systematic Review and Meta-Analysis

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