Assignment of unimodal probability distribution models for quantitative morphological phenotyping

Ghanegolmohammadi, Farzan; Ohnuki, Shinsuke; Ohya, Yoshikazu

doi:10.1186/s12915-022-01283-6

Cited by 6 publications

(10 citation statements)

References 49 publications

(57 reference statements)

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“…The true value is most likely the median of the distribution and falls within the range of the probability distribution. Based on this theory, we developed the UNIMO image analysis pipeline for accurate QMP of budding yeast (Ghanegolmohammadi et al 2022 ). Even yeast cells with simple structures have many different types of morphological features, including morphometric features such as cell size and shape, densitometric features such as brightness of each cellular compartment in microscopic images, and structural/spatial features (Fig.…”

Section: Unimo For Accurate Qmpmentioning

confidence: 99%

“…The influence of confounding factors, such as differences in microscopes, were excluded using a repeated experiment data set with detailed experimental logs, such as which microscope was used to take the micrographs. Of 501 morphological features, the distribution of experimental values of 490 features followed a unimodal distribution (Ghanegolmohammadi et al 2022 ). After applying unimodal probability distributions to almost all morphological features, the yeast morphology was accurately analyzed using generalized linear models in subsequent downstream analyses.…”

Section: Unimo For Accurate Qmpmentioning

confidence: 99%

“…The sensitivity of detection of morphological abnormalities remains low, preventing accurate detailed QMA. To overcome these problems, we recently developed the Unimodal Morphological data (UNIMO) image analysis pipeline that offers higher analytical power (Ghanegolmohammadi et al 2022 ). We proved that UNIMO is useful for accurate quantitative morphological phenotyping (QMP) of budding yeast.…”

Section: Introductionmentioning

confidence: 99%

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Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Ohya,

Ghanegolmohammadi,

Itto-Nakama

2024

FEMS Yeast Research

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Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

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Section: Unimo For Accurate Qmpmentioning

confidence: 99%

Section: Unimo For Accurate Qmpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Ohya,

Ghanegolmohammadi,

Itto-Nakama

2024

FEMS Yeast Research

Self Cite

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“…The industrial and engineering administration relies in its statistical analysis on the normal distribution, which is similar in shape to the shape of a bell [19]. The normal distribution curve is one of the most common curves in use [20,21]. In some statistical processes the data distributions follow a behaviour that constitutes an abnormal distribution [22] (Fig.…”

Section: Rayleigh Distributionmentioning

confidence: 99%

Rayleigh-Monte Carlo Method for Image Noise Detection and Elimination

Abbas,

Ruhaima,

Hayder

et al. 2023

Preprint

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The issue of identifying areas of distortion in images is a common problem encountered in digital media processing. Traditional filtering methods often rely on setting thresholds that are dependent on the specific image being processed, leading to imprecise results. Adaptive filters have been proposed as a solution, but they are not without their technical difficulties. This paper presents a study on the effectiveness of the Rayleigh distribution method in identifying distortion areas in images. Monte Carlo estimation was used to estimate the distribution of image content, and the results showed that the Rayleigh distribution is highly efficient at identifying areas of distortion. The filtering process was then applied only to the regions with deformation, while ensuring that all other features of the image were preserved. The novelty of this study lies in the use of the Rayleigh distribution method to identify areas of distortion in images. The purpose of this study was to improve the quality of images by using a more precise filtering method that only targets distorted areas. The theoretical framework of this study is based on the application of the Rayleigh distribution method in digital media processing. The approach used in this study involved the use of Monte Carlo estimation to determine the effectiveness of the Rayleigh distribution method. The findings of this study show that the Rayleigh distribution method is highly effective in identifying areas of distortion in images, leading to higher quality images than traditional filtering methods. The research findings have practical implications for digital media processing industries, where the quality of images is paramount. By using the Rayleigh distribution method, images can be processed more efficiently, leading to improved quality and reduced processing time. Socially, improved image quality can have a positive impact on areas such as medical imaging, where accurate and high-quality images are crucial for diagnosis and treatment. Overall, this study offers an original and effective solution to the problem of identifying areas of distortion in images. By using the Rayleigh distribution method, images can be processed more precisely, leading to higher quality results. The use of Monte Carlo estimation provides a solid foundation for future research in this area.

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“…The feature vector is transformed into category probability distribution by softmax function [17,18]; then,…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Evaluation Model of Mathematics Teaching Quality Based on Recurrent Neural Network

Dai¹,

Yang²

2022

Wireless Communications and Mobile Computing

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This study proposed an evaluation model of mathematics teaching quality under recurrent neural network for the sake of making the evaluation model of mathematics teaching quality have good fault tolerance. This model decomposes the initial data sequence of mathematics teaching quality evaluation into high- and low-frequency sequence by wavelet analysis and reconstructs it by using phase space. After introducing the recurrent neural network model, the data is reconstructed after model training, and the data mining is carried out for the evaluation of mathematics teaching quality. In the process of constructing the evaluation model, the evaluation index system should be constructed based on three dimensions firstly, and the evaluation index of association rules should be defined, so as to realize deep dig of data and obtain the phase space distribution of data and then carry out the constraint test of parameters to evaluate the mathematics teaching quality scientifically and accurately. After verification, it is known that the average values of training error and test error of the model proposed in this paper are 3.02% and 2.61%, and the average values of absolute error and relative error are 0.58 and 3.82%. This model can retain the valid data information in the initial sequence, and the evaluation results of mathematics teaching quality are relatively ideal, which greatly improves the efficiency and level of mathematics teaching.

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Assignment of unimodal probability distribution models for quantitative morphological phenotyping

Cited by 6 publications

References 49 publications

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Rayleigh-Monte Carlo Method for Image Noise Detection and Elimination

Evaluation Model of Mathematics Teaching Quality Based on Recurrent Neural Network

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