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Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.

ABSTRACT: Microbe organisms make up approximately 60% of the earth's living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict microbe organisms in the human body. A novel hybrid microbes classifier (HMC) is proposed in this study which is based on a decision tree classifier and extra tree classifier using voting criteria. Experiments involve different machine learning and deep learning models for detecting ten different living microforms of life. Results suggest that the proposed HMC approach achieves a 98% accuracy score, 98% geometric mean score, 97% precision score, and 97% Cohen Kappa score. The proposed model outperforms employed models, as well as, existing state-of-the-art models. Moreover, the k-fold cross-validation corroborates the results as well. The research helps microbiologists identify the type of microbe organisms with high accuracy and prevents many diseases through early detection.

SUBMITTER: Raza A

PROVIDER: S-EPMC10118187 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.

Raza Ali A Rustam Furqan F Siddiqui Hafeez Ur Rehman HUR Diez Isabel de la Torre IT Ashraf Imran I

PloS one 20230420 4

Microbe organisms make up approximately 60% of the earth's living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict micr ...[more]

PMID: 37079536

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Project description:Metabolic energy consumption has long been thought to play a major role in the aging process (Pearl, The rate of living. University of London Press, London, 1928). Across species, a gram of tissue expends approximately the same amount of energy during the lifespan on average (Speakman, J Exp Biol 208:1717-1730, 2005). Energy restriction has also been shown to increase the maximum lifespan (McCay et al. J Nutr 10:63-79, 1935) and to retard age-associated changes (Weindruch and Walford, The retardation of aging and disease by dietary restriction. CC Thomas, Springfield, 1988). However, there are significant exceptions to universal energy consumption during the lifespan, mainly found by interclass comparison (Ramsey et al. Free Rad Biol Med 29:946-968, 2000; Atanasov, Trakia J Sci 10(3):1-14, 2012). Here, we present a universal relation that relates lifespan energy consumption to several physiological variables, such as body mass, temperature and the ratio of heart rate to respiratory rate, which have been shown to be valid for [Formula: see text] species representing different classes of living organisms, from unicellular organisms to the largest mammals. This relation has an average scattered pattern restricted to factors of 2, with 95% ([Formula: see text]) of the organisms having departures of less than a factor of [Formula: see text] from the relation, despite the difference of [Formula: see text] orders of magnitude in body mass, reducing any possible interclass variation in the relation to only a geometrical factor. This result can be interpreted as supporting evidence for the existence of an approximately constant total number [Formula: see text] of respiration cycles per lifetime for all organisms studied, effectively predetermining the extension of life through the basic energetics of respiration (quantified by [Formula: see text]); this is an incentive to conduct future studies on the relation of such a constant number [Formula: see text] of cycles per lifetime due to the production rates of free radicals and oxidants or alternative mechanisms, which may yield definite constraints on the origin of aging.

Dataset Information

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.

Publications

Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.

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