A Machine Learning-Based algorithm for the assessment of clinical metabolomic fingerprints in Zika virus disease

Peláez, Enrique; Cordeiro, Fernanda Bertuccez; Medranda, Washington Cardenas; Barrett, Michael P.; Zambrano, Mildred; Regato, Mary

doi:10.1109/la-cci47412.2019.9037029

Cited by 7 publications

(11 citation statements)

References 22 publications

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“…ML with Logistic Regression and Support Vector Machine algorithms was also used in a preliminary study to differentiate ZIKV and healthy patients. The developed method obtained 98% of accuracy in the sample classification [89].…”

Section: Whole Mosquitomentioning

confidence: 97%

“…Dengue has been, undoubtedly, the most investigated arbovirus by metabolomics. In general, the works have focused on understanding the mechanism of these arboviruses infection and differentiation [61,63,[71][72][73][74][75], viral replication [76][77][78], disease progression and severity [60,[79][80][81][82][83][84][85], association with neurological disorders [65,86], and new purposes for diagnostic [62,64,[87][88][89]. Most of these studies are performed in vivo using samples of serum/plasma, urine, saliva, and mosquitoes (A. aegypti).…”

Section: Metabolomics Applications In Arbovirusesmentioning

confidence: 99%

“…Some researchers suggested that pilot studies comprise a sample equal to 20 provide good power analysis [27,90]. Overall, these investigations comprise reasonable study designs (n > 20), and just a couple of works were found with a sample size between 10 and 17 (pilot studies) [74,89], as presented in Supporting information Table S1. Despite working with large samples cohort, few studies provide detailed information on the design of the experiments, application of QC/QA (Quality Control/Quality Assurance), and method validation [60,61,64,65,83,84], according to MSI protocols.…”

Section: Metabolomics Applications In Arbovirusesmentioning

confidence: 99%

“…Our research group and collaborators have been dedicated to exploratory studies of metabolomics to understand the mechanisms of ZIKV infection in adult patients, and the metabolic changes caused in newborns diagnosed with ZIKV (manuscripts in preparation). So far, six studies have been found using untargeted metabolomics [65,75,78,86,88,89] involved in ZIKV infection and consequent neurological damage. The first study was published in 2017 by researchers from the Zika-UNICAMP Network, from the State University of Campinas (Brazil) and collaborators.…”

Section: Whole Mosquitomentioning

confidence: 99%

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Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review

Nunes

Canuto

2020

Electrophoresis

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Arboviruses, such as chikungunya, dengue, yellow fever, and zika, caused by the bite of the Aedes aegypti mosquito, have been a frequent public health problem, with a high incidence of outbreaks in tropical and subtropical countries. These diseases are easily confused with a flu‐like illness and present very similar symptoms, difficult to distinguish, and treat appropriately. The effects that these infections cause in the organism are fundamentally derived from complex metabolic processes. A prominent area of science that investigates the changes in the metabolism of complex organisms is the metabolomics. Metabolomics measures the metabolites produced or altered in biological organisms, through the use of robust analytical platforms, such as separation techniques hyphenated with mass spectrometry, combined with bioinformatics. This review article presents an overview of the basic concepts of metabolomics workflow and advances in this field, and compiles research articles that use this omic approach to study these arboviruses. In this context, the metabolomics is applied to search new therapies, understand the viral replication mechanisms, and access the host‐virus interactions.

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Section: Whole Mosquitomentioning

confidence: 97%

Section: Metabolomics Applications In Arbovirusesmentioning

confidence: 99%

Section: Metabolomics Applications In Arbovirusesmentioning

confidence: 99%

Section: Whole Mosquitomentioning

confidence: 99%

See 2 more Smart Citations

Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review

Nunes

Canuto

2020

Electrophoresis

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“…Support vector machines (SVMs) are classification and regression ML algorithms that separate data into two classes [ 165 , 166 ]. SVM algorithms map samples as data points from various classes in a high-dimensional feature space.…”

Section: 4ir Technologies and Plant Metabolomicsmentioning

confidence: 99%

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

et al. 2021

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Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

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Extending artificial intelligence research in the clinical domain: a theoretical perspective

2022

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Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

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A Machine Learning-Based algorithm for the assessment of clinical metabolomic fingerprints in Zika virus disease

Cited by 7 publications

References 22 publications

Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review

Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Extending artificial intelligence research in the clinical domain: a theoretical perspective

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