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 5 publications

(2 citation statements)

References 70 publications

(76 reference statements)

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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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Bioinformatics approaches for studying molecular sex differences in complex diseases

Loo,

Soudy,

Nasta

et al. 2024

Briefings in Bioinformatics

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Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

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

Cited by 5 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

Bioinformatics approaches for studying molecular sex differences in complex diseases

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