Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods

Jabeer, Amhar; Kocak, Aysegul; Akkas, Huseyin; Yenisert, Ferhan; Nalbantoğlu, Ufuk; Yousef, Malik; Bakir-Gungor, Burcu

doi:10.1109/asyu56188.2022.9925551

Cited by 3 publications

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“…It selects uncorrelated and informative features. Reflecting its popularity, mRMR has been widely used in cancer characterization from microbiome data [69][70][71] . In mRMR, the feature relevance is estimated using information gain (IG) or an F-test between a feature and the target classes, for categorical/discrete and continuous variables, respectively.…”

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confidence: 99%

“…Tree-based models, such as Random Forests and Gradient-Boosted Trees can inexpensively give an importance score (such as Gini importance) to the input features 73 . The best-ranked features can be used as input data for a more powerful downstream model, responsible for the final classification 70 . However, the features selected by these surrogate models may not be the most adequate for the predictor, as these operate under different constraints and optimization approaches.…”

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confidence: 99%

“…The most effective feature selection approach is dependent on the characteristics of the dataset, namely its sparsity and feature correlation, and the model used. One study compared the efficacy of statistical test-based methods, mRMR, a Fast Correlation Based Filter 79 , Conditional Mutual Information, and prior selection with Gradient Boosted Trees for dimensionality reduction in colorectal cancer prediction from metagenomic samples 70 . Statistical test-based methods, along with Gradient Boosted Trees selection achieved the best results when coupled with a Random Forest classifier; however, this may not hold true for other models or data: a feature selection approach should be selected on a case-by-case basis or with crossvalidation.…”

Section: Conditioned By Leads Tomentioning

confidence: 99%

“…Statistical test-based methods, along with Gradient Boosted Trees selection achieved the best results when coupled with a Random Forest classifier; however, this may not hold true for other models or data: a feature selection approach should be selected on a case-by-case basis or with crossvalidation. The authors also applied an ensemble selection, resulting in an 11% increase of AUC when compared to no feature selection 70 . Therefore, despite being computationally more expensive, ensemble approaches may provide more robust features selected by multiple different methods, and therefore suitable for a wider range of models.…”

Section: Conditioned By Leads Tomentioning

confidence: 99%

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A review of machine learning methods for cancer characterization from microbiome data

Teixeira,

Silva,

Ferreira

et al. 2024

npj Precis. Onc.

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Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

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Section: Conditioned By Leads Tomentioning

confidence: 99%

Section: Conditioned By Leads Tomentioning

confidence: 99%

Section: Conditioned By Leads Tomentioning

confidence: 99%

Section: Conditioned By Leads Tomentioning

confidence: 99%

See 2 more Smart Citations

A review of machine learning methods for cancer characterization from microbiome data

Teixeira,

Silva,

Ferreira

et al. 2024

npj Precis. Onc.

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Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data

Porreca,

Ibrahimi,

Maturo

et al. 2024

Journal of Medical Microbiology

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Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers. Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings. Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets. Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome. Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy. Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

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microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

Bakir-Gungor,

Temiz,

Jabeer

et al. 2023

Front. Microbiol.

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Numerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.

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Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods

Cited by 3 publications

References 0 publications

A review of machine learning methods for cancer characterization from microbiome data

A review of machine learning methods for cancer characterization from microbiome data

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data

microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

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