Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Celaya-Padilla, José M.; Villagrana-Bañuelos, Karen E.; Oropeza-Valdez, Juan José; Monárrez‐Espino, Joel; Castañeda-Delgado, Julio Enrique; Oostdam, Ana Sofía Herrera-Van; Fernández-Ruiz, Julio César; Ochoa-González, Fátima de Lourdes; Borrego, Juan Carlos; Enciso-Moreno, José Antonio; López, Jesús Adrián; López‐Hernández, Yamilé; Galván-Tejada, Carlos E.

doi:10.3390/diagnostics11122197

Cited by 4 publications

(2 citation statements)

References 70 publications

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“…The GA blast (the implementation of the GALGO model) output is then submitted to a forward selection process to obtain the model that performed best (could be one or more); then, this model or set of features is ready for utilization in an ML model. Forward selection is widely used in genetic algorithms as a complement to presenting the best possible model output of GALGO implementations, such as the following example: Alzheimer’s [ 28 ], COVID-19 [ 29 ] or diabetic retinopathy [ 30 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

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

et al. 2022

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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)”.

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Section: Materials and Methodsmentioning

confidence: 99%

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

et al. 2022

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“…All data analyses were done using Analyst 1.6.2 and MultiQuant 3.0.3. A detailed description about sample preparation and chromatographic methods was previously reported by our group 13 .…”

Section: Metabolites Measurementmentioning

confidence: 99%

COVID-19 outcome prediction by integrating clinical and metabolic data using machine learning algorithms

Villagrana-Bañuelos¹,

Maeda-Gutiérrez²,

Alcalá-Rmz³

et al. 2022

RIC

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Background:The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades. (REV INVEST CLIN.

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Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

Lázari

Oliveira²,

Macedo-da-Silva³

et al. 2023

Front. Anal. Sci.

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Identifying specific diagnostic and prognostic biological markers of COVID-19 can improve disease surveillance and therapeutic opportunities. Mass spectrometry combined with machine and deep learning techniques has been used to identify pathways that could be targeted therapeutically. Moreover, circulating biomarkers have been identified to detect individuals infected with SARS-CoV-2 and at high risk of hospitalization. In this review, we have surveyed studies that have combined mass spectrometry-based omics techniques (proteomics, lipdomics, and metabolomics) and machine learning/deep learning to understand COVID-19 pathogenesis. After a literature search, we show 42 studies that applied reproducible, accurate, and sensitive mass spectrometry-based analytical techniques and machine/deep learning methods for COVID-19 biomarker discovery and validation. We also demonstrate that multiomics data results in classification models with higher performance. Furthermore, we focus on the combination of MALDI-TOF Mass Spectrometry and machine learning as a diagnostic and prognostic tool already present in the clinics. Finally, we reiterate that despite advances in this field, more optimization in the analytical and computational parts, such as sample preparation, data acquisition, and data analysis, will improve biomarkers that can be used to obtain more accurate diagnostic and prognostic tools.

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Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Cited by 4 publications

References 70 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

COVID-19 outcome prediction by integrating clinical and metabolic data using machine learning algorithms

Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

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