Assessing seasonal climate variability impact on the malaria patient’s cases in the north of Côte d'Ivoire

Silué, Siélé; Kouassi, Adjon

doi:10.14738/aivp.96.11254

Cited by 1 publication

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“…We based cowpea cultivar clustering by submitting cowpea agro-morphometric and biochemistry data to z score normalization procedure. Indeed, the formula for calculating a z score is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation [25,26]. Z score data interpretation is as follows: the value of the z score tells you how many standard deviations you are away from the mean.…”

Section: Computational Statistical Analysismentioning

confidence: 99%

A computational statistical approach to assess cowpea [Vigna unguiculata (L.) Walp.] cultivars diversity and select elite genotypes from Agro-morphological and biochemical traits

Dago,

Assouman,

Martial Yao

et al. 2024

J Appl Pharm Sci

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Computational statistic approaches play an essential role in the evaluation and processing of agronomic, biological, and bio-medical big data. The complexity and large size of those data make computational statistics a crucial tool in bio-statistical analysis. Based on this evidence, we characterized phenotype performances of cowpea cultivar in the Northern of Côte d’Ivoire, developing our own computational statistical approach, exclusively in the R programming language. Several packages of R have been executed to assess cowpea cultivar agro-morphological and biochemical performances. Z-score clustering analysis revealed four groups of cowpeas based on agro-morphological parameters. K-mean clustering survey revealed four and two groups of cowpea cultivar respectively for agro-morphological and biochemical parameters. The Horn parallel analysis highlighted two, four, two and two agro-explanatory components and/or agro-morphological parameters as influencing data variability respectively in the first, second, third, and fourth groups of cowpea cultivars previously revealed by the k-mean analysis. The same analysis exhibited two components in terms of biochemical parameters, inducing the variability in the two cluster groups of cowpea cultivar revealed by the k-mean survey. Integrative analysis of the ANOVA test and Tukey’s multi-comparative analysis displayed yield (agro-morphological parameter) and cowpea energetic content (biochemical parameter) as main sources of cowpea cultivar phenotypical variability (P < 0.05). Of note, receiver operating characteristic predictive analysis showed the excellent performance of both cowpea yield and energetic content in selecting cowpea genetic germplasm area under the curve (AUC = 0.9). Considering as a whole, the present computational statistical approach shows excellent performance in the evaluation, characterization, and management of agro-morphological (yield parameter) and biochemical (energetic content) features of cowpea in genetic selection procedures.

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Section: Computational Statistical Analysismentioning

confidence: 99%