Overview of data preprocessing for machine learning applications in human microbiome research

Ibrahimi, Eliana; Lopes, Marta B.; Dhamo, Xhilda; Simeon, Andrea; Shigdel, Rajesh; Hron, Karel; Stres, Blaž; D’Elia, Domenica; Berland, Magali; Marcos-Zambrano, Laura Judith

doi:10.3389/fmicb.2023.1250909

Cited by 11 publications

(4 citation statements)

References 99 publications

(107 reference statements)

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“…Preprocessing of microbiome data is a crucial step in the analysis pipeline (Ibrahimi et al, 2023 ; Papoutsoglou et al, 2023 ). The microbiome data undergo several preprocessing steps.…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Novielli,

Romano,

Magarelli

et al. 2024

Front. Microbiol.

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BackgroundColorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.ResultsThe proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.DiscussionThese findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

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“…Preprocessing of microbiome data is a crucial step in the analysis pipeline (Ibrahimi et al, 2023 ; Papoutsoglou et al, 2023 ). The microbiome data undergo several preprocessing steps.…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Novielli,

Romano,

Magarelli

et al. 2024

Front. Microbiol.

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“…In order to conduct a robust analysis, the initial dataset has been strategically partitioned into a validation dataset and a test dataset to. This partitioning is designed to ensure a representative and unbiased evaluation of the models developed during the study (Ibrahimi et al, 2023 ). The validation dataset consists of 22 samples from the province of Salerno and 33 samples from the province of Caserta.…”

Section: Methodsmentioning

confidence: 99%

Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study

Magarelli,

Novielli,

De Filippis

et al. 2024

Front. Microbiol.

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Identifying the origin of a food product holds paramount importance in ensuring food safety, quality, and authenticity. Knowing where a food item comes from provides crucial information about its production methods, handling practices, and potential exposure to contaminants. Machine learning techniques play a pivotal role in this process by enabling the analysis of complex data sets to uncover patterns and associations that can reveal the geographical source of a food item. This study aims to investigate the potential use of explainable artificial intelligence for identifying the food origin. The case of study of Mozzarella di Bufala Campana PDO has been considered by examining the composition of the microbiota in each samples. Three different supervised machine learning algorithms have been compared and the best classifier model is represented by Random Forest with an Area Under the Curve (AUC) value of 0.93 and the top accuracy of 0.87. Machine learning models effectively classify origin, offering innovative ways to authenticate regional products and support local economies. Further research can explore microbiota analysis and extend applicability to diverse food products and contexts for enhanced accuracy and broader impact.

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“…This is even more important when considering metagenomic data. An overview of preprocessing steps for preparing microbiome sequencing data for machine learning is given by ( Ibrahimi et al, 2023 ). Normalization of sequencing results, for example, in transcriptomics, is essential, and a recent evaluation can be found here ( Ni and Qin, 2021 ).…”

Section: Challenges Of Deep Learning In Bioinformaticsmentioning

confidence: 99%

Deep learning in bioinformatics

Yousef,

ALLMER

2023

Turkish Journal of Biology

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Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.

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Overview of data preprocessing for machine learning applications in human microbiome research

Cited by 11 publications

References 99 publications

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study

Deep learning in bioinformatics

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