Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Barberis, Elettra; Khoso, Shahzaib; Sica, Antonio; Falasca, Marco; Giorgi, Ugo De; Dondero, Francesco; Afantitis, Antreas; Manfredi, Marcello

doi:10.3390/ijms231911269

“…The use of ML is proven to be an effective classifier and feature selection tool; however, the metabolomic community has some concerns with the lack of explanation on where does this biomarker’s significance come from. There are some methods that unveil these doubts; the statistical validation, for example, has the most widely known validator, which is the AUC or area under the curve [ 37 ]. In this proposal, this metric however will not be used; instead, the strict use of average accuracies in the feature selection with genetic algorithms is proposed as it comes from a validator given by the forward selection method provided by GALGO.…”

Section: Discussionmentioning

confidence: 99%

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

Morgan-Benita

¹

,

Sanchez-Reyna

²

,

Espino-Salinas

³

et al. 2022

View full text Add to dashboard Cite

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: “Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal” (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214–0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: “Cer(d18:1/24:1) i2”, “PC(20:3-OH/P-18:1)”, “Ganoderic acid C2”, “TG(16:0/17:1/18:1)” and “GPEtn(18:0/20:4)”.

show abstract

“…Machine learning is a subfield of artificial intelligence that involves the development of algorithms capable of learning from past data and generating predictions based on trained data. In contemporary times, machine learning has become a prevalent method for analyzing patterns of diseases utilize clinical and omics data due to its capacity to detect patterns from high dimensional datasets through a variety of algorithms [29][30][31][32]. The discipline of machine learning is commonly classified into four discrete subdomains, including supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning.…”

Section: Figurementioning

confidence: 99%

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Zehrh¹,

Habiba²,

Picco³

et al. 2023

Preprint

View full text Add to dashboard Cite

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

show abstract

“…Machine learning, on the other hand, is an area of artificial intelligence that allows the development of algorithms that can learn from historical data and make predictions based on trained data. Due to advantage of finding patterns from big dimensional data with variety of algorithms, machine learning is widely used for diagnosis purpose using clinical as well omics data [31][32][33][34]. Machine learning is mainly divided into four subfields including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.…”

Section: Metagenomics and Machine Learning For Diagnosis Of Diseasesmentioning

confidence: 99%

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Zehrh¹,

Habiba²,

Picco³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

show abstract

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Cited by 17 publications

References 95 publications

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases

Contact Info

Product

Resources

About