# Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Datasets and Preprocessing

#### 2.2. Machine Learning Model and Training Procedure

#### 2.3. Performance Metrics and Handling Class Imbalance

#### 2.4. Model Interpretability

## 3. Results

#### 3.1. Classification of Gut Microbiota from Inflammatory Bowel Disease Patients

#### 3.2. Classification of Gut Microbiota from Colorectal Cancer Patients

## 4. Discussion

#### 4.1. Stacking as a More Powerful Ensemble Method than Random Forest

#### 4.2. Meta Learner’s Role in Stacking

#### 4.3. Diversity of Base Learner’s Role in Stacking

#### 4.4. Overfitting Analysis

#### 4.5. Model Interpretation and Further Examples of Stacking

#### 4.6. Study Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AP | Average Precision |

AUROC | Area Under Receiver Operating Characteristic |

MCC | Matthews’s Correlation Coefficient |

SGD_LL | Stochastic Gradient Descent classifier with Logistic Loss |

SGD_HL | Stochastic Gradient Descent classifier with modified Huber Loss |

KNN | K-nearest Neighbors Classifier |

MLP | Multi-layer Perceptron |

QDA | Quadratic Discriminant Analysis |

RF | Random Forest |

HGBC | Histogram-based Gradient Boosting Classification |

## Appendix A

**Figure A1.**

**Overfitting analysis for inflammatory bowel disease dataset comparing stacked model’s and base learners’ performance separated by view during cross-validation.**Point values refer to median AP values and bars refer to estimated confidence intervals. Values for base learners trained on microbial view are aggregated together without distinction with regard to an individual base learner.

**Figure A2.**

**Overfitting analysis for colorectal cancer dataset comparing stacked model’s and base learners’ performance separated by view during cross-validation.**Point values refer to median AP values and bars refer to estimated confidence intervals. Values for base learners trained on microbial view are aggregated together without distinction with regard to an individual base learner.

**Figure A3.**

**Training and Test set PR curves for stacked model with Random Forest as a meta learner.**(

**a**) Inflammatory bowel disease dataset; (

**b**) Colorectal cancer dataset.

**Figure A4.**

**PR curves for stacked model applied on inflammatory bowel disease dataset with strict pre-filtering of microbial features.**(

**a**) Training set; (

**b**) Test set.

**Figure A5.**

**PR curves for stacked model applied on colorectal cancer dataset with strict pre-filtering of microbial features.**(

**a**) Training set; (

**b**) Test set.

**Figure A6.**

**PR curves for stacked model applied on inflammatory bowel disease dataset with additional clinical features.**(

**a**) Training set; (

**b**) Test set.

Feature Name | Type | Reason |
---|---|---|

diagnosis | Categorical | Training label |

external_id | Categorical | Irrelevant |

run_prefix | Categorical | Irrelevant |

body_site | Categorical | Training label leakage |

diseasesubtype | Categorical | Explicit training label leakage |

gastrointest_disord | Categorical | Explicit training label leakage |

host_subject_id | Categorical | Irrelevant |

disease_stat | Categorical | Implicit training label leakage |

disease_extent | Categorical | Implicit training label leakage |

ileal_invovlement | Categorical | Implicit training label leakage |

gastric_involvement | Categorical | Implicit training label leakage |

antibiotics | Categorical | Irrelevant |

steroids | Categorical | Irrelevant |

collection | Categorical | Irrelevant |

biologics | Categorical | Irrelevant |

birthdate | Categorical | Irrelevant |

body_habitat | Categorical | Implicit training label leakage |

body_product | Categorical | Irrelevant |

disease_duration | Numerical | Irrelevant |

immunosup | Categorical | Irrelevant |

mesalamine | Categorical | Irrelevant |

sample_type | Categorical | Irrelevant |

smoking | Categorical | Irrelevant |

Feature Name | Type | Description |
---|---|---|

Age | Numerical | Age in years |

b_cat | Categorical | Montreal classification of inflammatory bowel disease |

biopsy_location | Categorical | Location of biopsy |

inflammationstatus | Categorical | Inflammation status |

perianal | Categorical | Extension to anal area |

race | Categorical | Race |

sex | Categorical | Biological gender |

Feature Name | Type | Reason |
---|---|---|

Dx_Bin | Categorical | Training label |

Site | Categorical | Irrelevant |

Location | Categorical | Irrelevant |

Ethnic | Categorical | Irrelevant |

fit_result | Numerical | Training label leakage |

Hx_Prev | Categorical | Training label leakage |

Hx_Fam_CRC | Categorical | Training label leakage |

Hx_of_Polyps | Categorical | Training label leakage |

stage | Categorical | Training label leakage |

Gender | Categorical | Encoded as “Sex” |

White | Categorical | Encoded as “Ethnicity” |

Native | Categorical | Encoded as “Ethnicity” |

Black | Categorical | Encoded as “Ethnicity” |

Pacific | Categorical | Encoded as “Ethnicity” |

Asian | Categorical | Encoded as “Ethnicity” |

Other | Categorical | Encoded as “Ethnicity” |

Weight | Numerical | Encoded as “BMI” |

Height | Numerical | Encoded as “BMI” |

Feature Name | Type | Description |
---|---|---|

BMI | Numerical | Body Mass Index |

Age | Numerical | Age in years |

Smoke | Categorical | Smoking |

Diabetic | Categorical | Diabetes Melitus |

NSAID | Categorical | Anti-inflammatory medication |

Diabetes_Med | Categorical | Anti-diabetes medication |

Abx | Categorical | Antibiotic medication |

Ethnicity | Categorical | Race |

Sex | Categorical | Biological gender |

## References

- Cho, I.; Blaser, M.J. The human microbiome: At the interface of health and disease. Nat. Rev. Genet.
**2012**, 13, 260–270. [Google Scholar] [CrossRef] [Green Version] - Lynch, S.V.; Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med.
**2016**, 375, 2369–2379. [Google Scholar] [CrossRef] [Green Version] - Lv, G.; Cheng, N.; Wang, H. The gut microbiota, tumorigenesis, and liver diseases. Engineering
**2017**, 3, 110–114. [Google Scholar] [CrossRef] - Forbes, J.D.; Chen, C.Y.; Knox, N.C.; Marrie, R.A.; El-Gabalawy, H.; de Kievit, T.; Alfa, M.; Bernstein, C.N.; Van Domselaar, G. A comparative study of the gut microbiota in immune-mediated inflammatory diseases—Does a common dysbiosis exist? Microbiome
**2018**, 6, 1–15. [Google Scholar] [CrossRef] [Green Version] - Aldars-García, L.; Chaparro, M.; Gisbert, J.P. Systematic review: The gut microbiome and its potential clinical application in inflammatory bowel disease. Microorganisms
**2021**, 9, 977. [Google Scholar] [CrossRef] - Alexander, K.L.; Zhao, Q.; Reif, M.; Rosenberg, A.F.; Mannon, P.J.; Duck, L.W.; Elson, C.O. Human microbiota flagellins drive adaptive immune responses in Crohn’s disease. Gastroenterology
**2021**, 161, 522–535. [Google Scholar] [CrossRef] - Ghannam, R.B.; Techtmann, S.M. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Comput. Struct. Biotechnol. J.
**2021**, 19, 1092–1107. [Google Scholar] [CrossRef] - Sudhakar, P.; Machiels, K.; Verstockt, B.; Korcsmaros, T.; Vermeire, S. Computational biology and machine learning approaches to understand mechanistic microbiome-host interactions. Front. Microbiol.
**2021**, 12, 618856. [Google Scholar] [CrossRef] - Douglas, G.M.; Hansen, R.; Jones, C.; Dunn, K.A.; Comeau, A.M.; Bielawski, J.P.; Tayler, R.; El-Omar, E.M.; Russell, R.K.; Hold, G.L.; et al. Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease. Microbiome
**2018**, 6, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Knight, R.; Vrbanac, A.; Taylor, B.C.; Aksenov, A.; Callewaert, C.; Debelius, J.; Gonzalez, A.; Kosciolek, T.; McCall, L.I.; McDonald, D.; et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol.
**2018**, 16, 410–422. [Google Scholar] [CrossRef] [PubMed] - Heshiki, Y.; Vazquez-Uribe, R.; Li, J.; Ni, Y.; Quainoo, S.; Imamovic, L.; Li, J.; Sørensen, M.; Chow, B.K.; Weiss, G.J.; et al. Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome
**2020**, 8, 1–14. [Google Scholar] [CrossRef] [Green Version] - Vilas-Boas, F.; Ribeiro, T.; Afonso, J.; Cardoso, H.; Lopes, S.; Moutinho-Ribeiro, P.; Ferreira, J.; Mascarenhas-Saraiva, M.; Macedo, G. Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study. Diagnostics
**2022**, 12, 2041. [Google Scholar] [CrossRef] - Mascarenhas, M.; Afonso, J.; Ribeiro, T.; Cardoso, H.; Andrade, P.; Ferreira, J.P.; Saraiva, M.M.; Macedo, G. Performance of a deep learning system for automatic diagnosis of protruding lesions in colon capsule endoscopy. Diagnostics
**2022**, 12, 1445. [Google Scholar] [CrossRef] - Nogueira-Rodríguez, A.; Reboiro-Jato, M.; Glez-Peña, D.; López-Fernández, H. Performance of Convolutional Neural Networks for Polyp Localization on Public Colonoscopy Image Datasets. Diagnostics
**2022**, 12, 898. [Google Scholar] [CrossRef] - Wolpert, D.H.; Macready, W.G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput.
**1997**, 1, 67–82. [Google Scholar] [CrossRef] [Green Version] - Pasolli, E.; Truong, D.T.; Malik, F.; Waldron, L.; Segata, N. Machine learning meta-analysis of large metagenomic datasets: Tools and biological insights. PLoS Comput. Biol.
**2016**, 12, e1004977. [Google Scholar] [CrossRef] [Green Version] - Topçuoğlu, B.D.; Lesniak, N.A.; Ruffin IV, M.T.; Wiens, J.; Schloss, P.D. A framework for effective application of machine learning to microbiome-based classification problems. MBio
**2020**, 11, e00434-20. [Google Scholar] [CrossRef] - Bourel, M.; Segura, A. Multiclass classification methods in ecology. Ecol. Indic.
**2018**, 85, 1012–1021. [Google Scholar] [CrossRef] - Statnikov, A.; Henaff, M.; Narendra, V.; Konganti, K.; Li, Z.; Yang, L.; Pei, Z.; Blaser, M.J.; Aliferis, C.F.; Alekseyenko, A.V. A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome
**2013**, 1, 1–12. [Google Scholar] [CrossRef] [Green Version] - Caruana, R.; Lou, Y.; Gehrke, J.; Koch, P.; Sturm, M.; Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 10–13 August 2015; pp. 1721–1730. [Google Scholar]
- Nauta, M.; Walsh, R.; Dubowski, A.; Seifert, C. Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis. Diagnostics
**2021**, 12, 40. [Google Scholar] [CrossRef] - Chen, T.; Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar]
- Breiman, L. Random forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] [Green Version] - Lou, Y.; Caruana, R.; Gehrke, J.; Hooker, G. Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 11–14 August 2013; pp. 623–631. [Google Scholar]
- Wolpert, D.H. Stacked generalization. Neural Netw.
**1992**, 5, 241–259. [Google Scholar] [CrossRef] - Džeroski, S.; Ženko, B. Is combining classifiers with stacking better than selecting the best one? Mach. Learn.
**2004**, 54, 255–273. [Google Scholar] [CrossRef] [Green Version] - Sesmero, M.P.; Ledezma, A.I.; Sanchis, A. Generating ensembles of heterogeneous classifiers using stacked generalization. WIley Interdiscip. Rev. Data Min. Knowl. Discov.
**2015**, 5, 21–34. [Google Scholar] [CrossRef] - Chen, Y.; Wang, H.; Lu, W.; Wu, T.; Yuan, W.; Zhu, J.; Lee, Y.K.; Zhao, J.; Zhang, H.; Chen, W. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes
**2022**, 14, 2025016. [Google Scholar] [CrossRef] - Gevers, D.; Kugathasan, S.; Knights, D.; Kostic, A.D.; Knight, R.; Xavier, R.J. A microbiome foundation for the study of Crohn’s disease. Cell Host Microbe
**2017**, 21, 301–304. [Google Scholar] [CrossRef] [Green Version] - Gevers, D.; Kugathasan, S.; Denson, L.A.; Vázquez-Baeza, Y.; Van Treuren, W.; Ren, B.; Schwager, E.; Knights, D.; Song, S.J.; Yassour, M.; et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe
**2014**, 15, 382–392. [Google Scholar] [CrossRef] [Green Version] - Baxter, N.T.; Ruffin, M.T.; Rogers, M.A.; Schloss, P.D. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med.
**2016**, 8, 1–10. [Google Scholar] [CrossRef] [Green Version] - Battaglia, T. A Repository for Large-Scale Microbiome Datasets. 2022. Available online: https://github.com/twbattaglia/MicrobeDS (accessed on 13 October 2022).
- The Laboratory of Pat Schloss at the University of Michigan. 2022. Available online: https://github.com/SchlossLab/Baxter_glne007Modeling_GenomeMed_2015 (accessed on 13 October 2022).
- Yeo, I.K.; Johnson, R.A. A new family of power transformations to improve normality or symmetry. Biometrika
**2000**, 87, 954–959. [Google Scholar] [CrossRef] - Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res.
**2011**, 12, 2825–2830. [Google Scholar] - Branco, P.; Torgo, L.; Ribeiro, R.P. A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. Csur
**2016**, 49, 1–50. [Google Scholar] [CrossRef] - Ozenne, B.; Subtil, F.; Maucort-Boulch, D. The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. J. Clin. Epidemiol.
**2015**, 68, 855–859. [Google Scholar] [CrossRef] [PubMed] - Su, W.; Yuan, Y.; Zhu, M. A relationship between the average precision and the area under the ROC curve. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval, Northampton, MA, USA, 27–30 September 2015; pp. 349–352. [Google Scholar]
- Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom.
**2020**, 21, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Zadrozny, B.; Langford, J.; Abe, N. Cost-sensitive learning by cost-proportionate example weighting. In Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, FL, USA, 22 November 2003; pp. 435–442. [Google Scholar]
- Chang, C.C.; Huang, T.H.; Shueng, P.W.; Chen, S.H.; Chen, C.C.; Lu, C.J.; Tseng, Y.J. Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors. Int. J. Environ. Res. Public Health
**2021**, 18, 12499. [Google Scholar] [CrossRef] - Ting, K.M.; Witten, I.H. Issues in stacked generalization. J. Artif. Intell. Res.
**1999**, 10, 271–289. [Google Scholar] [CrossRef] [Green Version] - Ghaemi, M.S.; DiGiulio, D.B.; Contrepois, K.; Callahan, B.; Ngo, T.T.; Lee-McMullen, B.; Lehallier, B.; Robaczewska, A.; Mcilwain, D.; Rosenberg-Hasson, Y.; et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics
**2019**, 35, 95–103. [Google Scholar] [CrossRef] [Green Version] - Klang, E.; Freeman, R.; Levin, M.A.; Soffer, S.; Barash, Y.; Lahat, A. Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study. Diagnostics
**2021**, 11, 2102. [Google Scholar] [CrossRef] - Baumgart, D.C. The diagnosis and treatment of Crohn’s disease and ulcerative colitis. Dtsch. ÄRzteblatt Int.
**2009**, 106, 123. [Google Scholar] [CrossRef] - Sartor, R.B. Mechanisms of disease: Pathogenesis of Crohn’s disease and ulcerative colitis. Nat. Clin. Pract. Gastroenterol. Hepatol.
**2006**, 3, 390–407. [Google Scholar] [CrossRef] - Silva, M.; Pratas, D.; Pinho, A.J. AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models. Entropy
**2021**, 23, 530. [Google Scholar] [CrossRef] - Janitza, S.; Hornung, R. On the overestimation of random forest’s out-of-bag error. PloS ONE
**2018**, 13, e0201904. [Google Scholar] [CrossRef] - Guyon, I.; Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res.
**2003**, 3, 1157–1182. [Google Scholar] - Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. Methodol.
**1995**, 57, 289–300. [Google Scholar] [CrossRef] - Benjamini, Y.; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat.
**2001**, 29, 1165–1188. [Google Scholar] [CrossRef] - Zhu, Q.; Li, B.; He, T.; Li, G.; Jiang, X. Robust biomarker discovery for microbiome-wide association studies. Methods
**2020**, 173, 44–51. [Google Scholar] [CrossRef] [PubMed] - Bakir-Gungor, B.; Hacılar, H.; Jabeer, A.; Nalbantoglu, O.U.; Aran, O.; Yousef, M. Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods. PeerJ
**2022**, 10, e13205. [Google Scholar] [CrossRef] [PubMed] - Sharma, D.; Paterson, A.D.; Xu, W. TaxoNN: Ensemble of neural networks on stratified microbiome data for disease prediction. Bioinformatics
**2020**, 36, 4544–4550. [Google Scholar] [CrossRef] - Mulenga, M.; Kareem, S.A.; Sabri, A.Q.M.; Seera, M. Stacking and chaining of normalization methods in deep learning-based classification of colorectal cancer using gut microbiome data. IEEE Access
**2021**, 9, 97296–97319. [Google Scholar] [CrossRef]

**Figure 1.**

**Multi-view stacked generalization framework’s illustrative example.**Methodologically this computational framework’s example consists of three base machine learning models and a meta learner. The predictions of the base learners on validation folds are stacked together as meta features and presented for training to the meta learner model which outputs the final prediction. The illustrative example pipeline was trained on two subsets of features that allowed multi-view setting by training base learners 1 and 2 on features of view 1 and the base learner 3 on features of view 2. For the sake of simplicity, the example using only two views and three base learners is explained. In principle, number of views and base learners is not limited.

**Figure 2.**

**Model performance comparison between base learners and stacked model, inflammatory bowel disease dataset.**(

**a**) Precision-Recall (PR) curves and corresponding Average Precision (AP) values of base classifiers, SoftVote classifier and the stacked classifier applied on training set; (

**b**) PR curves and corresponding AP values of base classifiers, SoftVote classifier and the stacked classifier applied on test set; (

**c**) Matthews’s Correlation Coefficient (MCC) values matrix heatmap of base classifier predictions, classification labels and the stacked classifier predictions applied on training set; (

**d**) MCC values matrix heatmap of base classifier predictions, classification labels and the stacked classifier predictions applied on test set; (

**e**) AP values of each model during cross-validation on training and validation sets. Red horizontal line refers to AP value obtained from a random classifier.

**Figure 3.**

**Sorted feature importance values, inflammatory bowel disease dataset, training set.**(

**a**) Normalized regression weights obtained from a meta learner used in stacked classifier; (

**b**) Violin plots of permutation feature importance values obtained from a stacked classifier. Red vertical line represents zero importance value.

**Figure 4.**

**Model performance comparison between base learners and stacked model, colorectal cancer dataset.**(

**a**) PR curves and corresponding AP values of base classifiers, SoftVote classifier and the stacked classifier applied on training set; (

**b**) PR curves and corresponding AP values of base classifiers, SoftVote classifier and stacked classifier applied on test set; (

**c**) MCC values matrix heatmap of base classifier predictions, classification labels and the stacked classifier predictions applied on training set; (

**d**) MCC values matrix heatmap of base classifier predictions, classification labels and stacked classifier predictions applied on test set; (

**e**) AP values of each model during cross-validation on training and validation sets. Red horizontal line refers to AP value obtained from a random classifier.

**Figure 5.**

**Sorted feature importance values, colorectal cancer dataset, training set.**(

**a**) Normalized regression weights obtained from a meta learner used in stacked classifier; (

**b**) Violin plots of permutation feature importance values obtained from a stacked classifier. Red vertical line represents zero importance value.

Clinical View Features | Microbial View Features | |||
---|---|---|---|---|

# Numerical | # Categorical | # Total | # Unique Genera | |

Inflammatory bowel disease | 1 | 6 | 6737 | 533 |

Colorectal cancer | 2 | 7 | 5982 | 239 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Imangaliyev, S.; Schlötterer, J.; Meyer, F.; Seifert, C.
Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data. *Diagnostics* **2022**, *12*, 2514.
https://doi.org/10.3390/diagnostics12102514

**AMA Style**

Imangaliyev S, Schlötterer J, Meyer F, Seifert C.
Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data. *Diagnostics*. 2022; 12(10):2514.
https://doi.org/10.3390/diagnostics12102514

**Chicago/Turabian Style**

Imangaliyev, Sultan, Jörg Schlötterer, Folker Meyer, and Christin Seifert.
2022. "Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data" *Diagnostics* 12, no. 10: 2514.
https://doi.org/10.3390/diagnostics12102514