Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease

Zubnar, Andrea; Mladenović, Katarina G.; Lončar-Turukalo, Tatjana; Brdar, Sanja

doi:10.3233/shti210591

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent studies have highlighted the advantages of implementing the ML pipeline on SM data to understand microbial taxa, identify signatures for disease identification and diagnose complex medical conditions, particularly for gut microbiome-related diseases. These studies demonstrate the following key benefits: (I) Improved Classification Accuracy to taxa associated with IBD: Mihajlović et al ( 2021 ) employed a random forest (RF) model to classify Inflammatory Bowel Disease (IBD), achieving an average F1 score of 91%. This underscores the robust connection between IBD and the gut microbiome, showcasing how ML can enhance diagnostic accuracy in complex diseases.…”

Section: Machine Learning For Microbiome Data Analysismentioning

confidence: 99%

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Kumar,

Lorusso,

Fosso

et al. 2024

Front. Microbiol.

View full text Add to dashboard Cite

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

show abstract

Section: Machine Learning For Microbiome Data Analysismentioning

confidence: 99%

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Kumar,

Lorusso,

Fosso

et al. 2024

Front. Microbiol.

View full text Add to dashboard Cite

show abstract

“…The employed learning techniques are KNN and SVM where SVM proves to be superior with a 92% accuracy score. The inflammatory bowel disease prediction using machine learning techniques is proposed in the study [28]. The metagenomic dataset on inflammatory bowel disease multi-omics is utilized for machine learning model building and experimental research evaluations.…”

Section: Related Workmentioning

confidence: 99%

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Raza

Rustam

Siddiqui

et al. 2022

Genes

View full text Add to dashboard Cite

Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.

show abstract

“…Automated image recognition using deep learning methods also has been applied in the endoscopic images and pathological images recognition of IBD ( 21 , 26 – 28 ). Moreover, in search of new well-performing markers at the gene and microbiome level, the ML methods showed the greatest contribution in variables screening ( 29 , 30 ). Based on the development of these techniques, introducing ML into the area of UC evaluation can provide a promising approach for researchers.…”

Section: Introductionmentioning

confidence: 99%

Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation

Wang

et al. 2022

Front. Med.

View full text Add to dashboard Cite

BackgroundEndoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs.MethodsThe patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models.ResultsA total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values.ConclusionThis proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.

show abstract

Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease

Cited by 5 publications

References 6 publications

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation

Contact Info

Product

Resources

About