Immunological Profiling of Paediatric Inflammatory Bowel Disease Using Unsupervised Machine Learning

Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm.

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Machine Learning–Based Gene Prioritization Identifies Novel Candidate Risk Genes for Inflammatory Bowel Disease

Inflammatory Bowel Diseases ◽

10.1097/mib.0000000000001222 ◽

2017 ◽

Vol 23 (9) ◽

pp. 1516-1523 ◽

Cited By ~ 10

Author(s):

Ofer Isakov ◽

Iris Dotan ◽

Shay Ben-Shachar

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Gene Prioritization ◽

Risk Genes ◽

Inflammatory Bowel

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Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach

F1000Research ◽

10.12688/f1000research.15091.2 ◽

2019 ◽

Vol 7 ◽

pp. 702

Author(s):

Pedro Morell Miranda ◽

Francesca Bertolini ◽

Haja N. Kadarmideen

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Random Forest ◽

Bowel Disease ◽

Gut Microbiome ◽

Predictive Power ◽

Learning Approach ◽

Important Species ◽

Machine Learning Approach ◽

Inflammatory Bowel

Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.

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Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

Scientific Reports ◽

10.1038/s41598-017-02606-2 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 31

Author(s):

E. Mossotto ◽

J. J. Ashton ◽

T. Coelho ◽

R. M. Beattie ◽

B. D. MacArthur ◽

...

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Inflammatory Bowel

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Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Genes ◽

10.3390/genes12091438 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1438

Author(s):

Biljana Stankovic ◽

Nikola Kotur ◽

Gordana Nikcevic ◽

Vladimir Gasic ◽

Branka Zukic ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Disease Diagnosis ◽

Omics Data ◽

Current Application ◽

Inflammatory Bowel ◽

Future Outcomes ◽

Clinical Records

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

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Machine Learning Prediction Model for Inflammatory Bowel Disease Based on Laboratory Markers. Working Model in a Discovery Cohort Study

Journal of Clinical Medicine ◽

10.3390/jcm10204745 ◽

2021 ◽

Vol 10 (20) ◽

pp. 4745

Author(s):

Sebastian Kraszewski ◽

Witold Szczurek ◽

Julia Szymczak ◽

Monika Reguła ◽

Katarzyna Neubauer

Keyword(s):

Machine Learning ◽

Inflammatory Bowel Disease ◽

Prediction Model ◽

Bowel Disease ◽

Prediction Models ◽

Wide Spectrum ◽

Clinical Problem ◽

Disease Diagnosis ◽

Average Precision ◽

Inflammatory Bowel

Inflammatory bowel disease (IBD) is a chronic, incurable disease involving the gastrointestinal tract. It is characterized by complex, unclear pathogenesis, increased prevalence worldwide, and a wide spectrum of extraintestinal manifestations and comorbidities. Recognition of IBD remains challenging and delays in disease diagnosis still poses a significant clinical problem as it negatively impacts disease outcome. The main diagnostic tool in IBD continues to be invasive endoscopy. We aimed to create an IBD machine learning prediction model based on routinely performed blood, urine, and fecal tests. Based on historical patients’ data (702 medical records: 319 records from 180 patients with ulcerative colitis (UC) and 383 records from 192 patients with Crohn’s disease (CD)), and using a few simple machine learning classificators, we optimized necessary hyperparameters in order to get reliable few-features prediction models separately for CD and UC. Most robust classificators belonging to the random forest family obtained 97% and 91% mean average precision for CD and UC, respectively. For comparison, the commonly used one-parameter approach based on the C-reactive protein (CRP) level demonstrated only 81% and 61% average precision for CD and UC, respectively. Results of our study suggest that machine learning prediction models based on basic blood, urine, and fecal markers may with high accuracy support the diagnosis of IBD. However, the test requires validation in a prospective cohort.

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Detection of Unspecified Inflammatory Bowel Disease, Crohn’s Disease and Ulcerative Colitis via Metabolite Analysis and Machine Learning

Journal of Advances in Medicine and Medical Research ◽

10.9734/jammr/2021/v33i330823 ◽

2021 ◽

pp. 79-104

Author(s):

Elliot Kim ◽

Valentina L. Kouznetsova ◽

Igor F. Tsigelny

Keyword(s):

Machine Learning ◽

Ulcerative Colitis ◽

Inflammatory Bowel Disease ◽

Crohn’S Disease ◽

Crohn's Disease ◽

Bowel Disease ◽

Molecular Mechanisms ◽

Current Standard ◽

Inflammatory Bowel ◽

Accuracy Of Prediction

The current standard of inflammatory bowel disease (IBD), especially, Crohn’s disease (CD), diagnosis is set through an invasive endoscopy procedure. However, serum metabolites hold potential as useful biomarkers for non-invasive diagnosis and treatment of IBDs. The goal of this research was to elucidate the biomarkers including metabolites and genes related to IBDs, to show their distinguishing and common features, and to create a machine-learning (ML) model for recognition of each disease. We explored metabolic pathways and gene–metabolite networks related to unspecified-IBD (uIBD), Crohn’s diseaseand ulcerative colitis (UC). P38 MAPK, ERK1/2, AMPK, and proinsulin were found to be closely related to the pathology of IBDs. The best performing ML model, trained on filtered disease-specific metabolite datasets, was able to predict metabolite class with 92.17% accuracy. Through examination of IBD-related serum, significant relationships between the inputted metabolites and certain metabolic and signaling pathways were found, which can be pinpointed and used to increase accuracy of disease diagnoses. Development of a ML model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of the IBDs. Our results elucidate a large variety of metabolites, genes, and pathways that could be used for better understanding of IBDs’ molecular mechanisms.

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